From ed2a7f9c18caa54addec0c1f8c40214aefab3958 Mon Sep 17 00:00:00 2001 From: Danush Shekar <67160286+CodexForster@users.noreply.github.com> Date: Sun, 17 Aug 2025 00:33:19 -0500 Subject: [PATCH 01/13] Corrected muWP and removed redundant aliases --- ControlRegions/WZ/2022EE_v12/aliases.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ControlRegions/WZ/2022EE_v12/aliases.py b/ControlRegions/WZ/2022EE_v12/aliases.py index f06c74c9..b2f06dd0 100644 --- a/ControlRegions/WZ/2022EE_v12/aliases.py +++ b/ControlRegions/WZ/2022EE_v12/aliases.py @@ -17,7 +17,8 @@ # LepCut3l__ele_wp90iso__mu_cut_TightID_POG eleWP = 'wp90iso' -muWP = 'cut_TightID_POG' +muWP = 'cut_Tight_HWW' +#muWP = 'cut_TightID_POG' aliases['LepWPCut'] = { 'expr': 'LepCut3l__ele_'+eleWP+'__mu_'+muWP, @@ -177,6 +178,8 @@ 'samples': mc } +# The following aliases have already been defined. +""" # WH3l_mOSll for data aliases['WH3l_mOSll'] = { 'linesToAdd' : [f'#include "{configurations}/ControlRegions/WZ/macros/mOS_ll.cc"'], @@ -216,3 +219,4 @@ 'args' : 'nLepton,Lepton_pt,Lepton_eta,Lepton_phi,Lepton_pdgId', 'samples' : ['DATA'], } +""" From fe3ea07b6ea55f78a7e5011a651a2d6a444e218a Mon Sep 17 00:00:00 2001 From: CodexForster Date: Wed, 17 Sep 2025 14:56:17 +0200 Subject: [PATCH 02/13] 2023 WZ CR --- ControlRegions/WZ/2023_v12/aliases.py | 223 ++++++++++++++++++++ ControlRegions/WZ/2023_v12/configuration.py | 94 +++++++++ ControlRegions/WZ/2023_v12/cuts.py | 28 +++ ControlRegions/WZ/2023_v12/nuisances.py | 52 +++++ ControlRegions/WZ/2023_v12/plot.py | 79 +++++++ ControlRegions/WZ/2023_v12/samples.py | 201 ++++++++++++++++++ ControlRegions/WZ/2023_v12/structure.py | 33 +++ ControlRegions/WZ/2023_v12/variables.py | 158 ++++++++++++++ 8 files changed, 868 insertions(+) create mode 100644 ControlRegions/WZ/2023_v12/aliases.py create mode 100644 ControlRegions/WZ/2023_v12/configuration.py create mode 100644 ControlRegions/WZ/2023_v12/cuts.py create mode 100644 ControlRegions/WZ/2023_v12/nuisances.py create mode 100644 ControlRegions/WZ/2023_v12/plot.py create mode 100644 ControlRegions/WZ/2023_v12/samples.py create mode 100644 ControlRegions/WZ/2023_v12/structure.py create mode 100644 ControlRegions/WZ/2023_v12/variables.py diff --git a/ControlRegions/WZ/2023_v12/aliases.py b/ControlRegions/WZ/2023_v12/aliases.py new file mode 100644 index 00000000..d909ad70 --- /dev/null +++ b/ControlRegions/WZ/2023_v12/aliases.py @@ -0,0 +1,223 @@ +import os +import copy +import inspect + +# /afs/cern.ch/user/n/ntrevisa/work/latinos/Run3/PlotsConfigurationsRun3/ControlRegions/WZ/2023_v12 +configurations = os.path.realpath(inspect.getfile(inspect.currentframe())) # this file +configurations = os.path.dirname(configurations) # 2023_v12 +configurations = os.path.dirname(configurations) # WZ +configurations = os.path.dirname(configurations) # ControlRegions +configurations = os.path.dirname(configurations) # PlotsConfigurationsRun3 + +aliases = {} +aliases = OrderedDict() + +mc = [skey for skey in samples if skey not in ('Fake', 'DATA', 'Dyemb', 'DATA_EG', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] +mc_emb = [skey for skey in samples if skey not in ('Fake', 'DATA', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] + +# LepCut3l__ele_wp90iso__mu_cut_TightID_POG +eleWP = 'wp90iso' +muWP = 'cut_Tight_HWW' +#muWP = 'cut_TightID_POG' + +aliases['LepWPCut'] = { + 'expr': 'LepCut3l__ele_'+eleWP+'__mu_'+muWP, + 'samples': mc + ['DATA'], +} + +aliases['LepWPSF'] = { + 'expr': 'LepSF3l__ele_'+eleWP+'__mu_'+muWP, + 'samples': mc +} + +# gen-matching to prompt only (GenLepMatch3l matches to *any* gen lepton) +aliases['PromptGenLepMatch3l'] = { + 'expr': 'Alt(Lepton_promptgenmatched, 0, 0) * Alt(Lepton_promptgenmatched, 1, 0) * Alt(Lepton_promptgenmatched, 2, 0)', + 'samples': mc +} + +# Jet bins +# using Alt(CleanJet_pt, n, 0) instead of Sum(CleanJet_pt >= 30) because jet pt ordering is not strictly followed in JES-varied samples + +# No jet with pt > 30 GeV +aliases['zeroJet'] = { + 'expr': 'Alt(CleanJet_pt, 0, 0) < 30.' +} + +aliases['oneJet'] = { + 'expr': 'Alt(CleanJet_pt, 0, 0) > 30.' +} + +aliases['multiJet'] = { + 'expr': 'Alt(CleanJet_pt, 1, 0) > 30.' +} + +aliases['noJetInHorn'] = { + 'expr' : 'Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', +} + +######################################################################## +# B-Tagging WP: https://btv-wiki.docs.cern.ch/ScaleFactors/Run3Summer23/ +######################################################################## + +# Algo / WP / WP cut +btagging_WPs = { + "DeepFlavB" : { + "loose" : "0.0479", + "medium" : "0.2431", + "tight" : "0.6553", + "xtight" : "0.7667", + "xxtight" : "0.9459", + }, + "RobustParTAK4B" : { + "loose" : "0.0358", + "medium" : "0.1917", + "tight" : "0.6172", + "xtight" : "0.7515", + "xxtight" : "0.9659", + }, + "PNetB" : { + "loose" : "0.0681", + "medium" : "0.3487", + "tight" : "0.7969", + "xtight" : "0.8882", + "xxtight" : "0.9883", + } +} + +# Algo / SF name +btagging_SFs = { + "DeepFlavB" : "deepjet", + "RobustParTAK4B" : "partTransformer", + "PNetB" : "deepjet", +} + +# Algorithm and WP selection +bAlgo = 'DeepFlavB' # ['DeepFlavB','RobustParTAK4B','PNetB'] +WP = 'loose' # ['loose','medium','tight','xtight','xxtight'] + +# Access information from dictionaries +bWP = btagging_WPs[bAlgo][WP] +bSF = btagging_SFs[bAlgo] + +# B tagging selections and scale factors +aliases['bVeto'] = { + 'expr': f'Sum(CleanJet_pt > 20. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) == 0' +} + +aliases['bVetoSF'] = { + 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>20 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<20 || abs(CleanJet_eta)>2.5))))'.format(bSF), + 'samples': mc +} + +aliases['bReq'] = { + 'expr': f'Sum(CleanJet_pt > 30. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) >= 1' +} + +aliases['bReqSF'] = { + 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>30 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<30 || abs(CleanJet_eta)>2.5))))'.format(bSF), + 'samples': mc +} + +# Top control region +aliases['topcr'] = { + 'expr': 'mtw2>30 && mll>50 && ((zeroJet && !bVeto) || bReq)' +} + +# WW control region +aliases['wwcr'] = { + 'expr': 'mth>60 && mtw2>30 && mll>100 && bVeto' +} + +# Overall b tag SF +aliases['btagSF'] = { + 'expr': '(bVeto || (topcr && zeroJet))*bVetoSF + (topcr && !zeroJet)*bReqSF', + 'samples': mc +} + +# Systematic uncertainty variations +for shift in ['jes','lf','hf','lfstats1','lfstats2','hfstats1','hfstats2','cferr1','cferr2']: + + for targ in ['bVeto', 'bReq']: + alias = aliases['%sSF%sup' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) + alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_up_%s' % shift) + + alias = aliases['%sSF%sdown' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) + alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_down_%s' % shift) + + aliases['btagSF%sup' % shift] = { + 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'up'), + 'samples': mc + } + + aliases['btagSF%sdown' % shift] = { + 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'down'), + 'samples': mc + } + +########################################################################## +# End of b tagging +########################################################################## + +# Data/MC scale factors and systematic uncertainties +aliases['SFweight'] = { + 'expr': ' * '.join(['SFweight3l', 'LepWPCut', 'LepWPSF','btagSF']), + 'samples': mc +} + +aliases['SFweightEleUp'] = { + 'expr': 'LepSF3l__ele_'+eleWP+'__Up', + 'samples': mc +} +aliases['SFweightEleDown'] = { + 'expr': 'LepSF3l__ele_'+eleWP+'__Down', + 'samples': mc +} +aliases['SFweightMuUp'] = { + 'expr': 'LepSF3l__mu_'+muWP+'__Up', + 'samples': mc +} +aliases['SFweightMuDown'] = { + 'expr': 'LepSF3l__mu_'+muWP+'__Down', + 'samples': mc +} + +# # WH3l_mOSll for data +# aliases['WH3l_mOSll'] = { +# 'linesToAdd' : [f'#include "{configurations}/ControlRegions/WZ/macros/mOS_ll.cc"'], +# 'class' : 'mOS_ll', +# 'args' : 'nLepton,Lepton_pt,Lepton_eta,Lepton_phi,Lepton_pdgId', +# 'samples' : ['DATA'], +# } + +# # WH3l_mlll for data +# aliases['WH3l_mlll'] = { +# 'linesToAdd' : [f'#include "{configurations}/ControlRegions/WZ/macros/m_lll.cc"'], +# 'class' : 'm_lll', +# 'args' : 'nLepton,Lepton_pt,Lepton_eta,Lepton_phi,Lepton_pdgId', +# 'samples' : ['DATA'], +# } + +# # WH3l_ZVeto for data +# aliases['WH3l_ZVeto'] = { +# 'linesToAdd' : [f'#include "{configurations}/ControlRegions/WZ/macros/z_veto.cc"'], +# 'class' : 'z_veto', +# 'args' : 'nLepton,Lepton_pt,Lepton_eta,Lepton_phi,Lepton_pdgId', +# 'samples' : ['DATA'], +# } + +# # WH3l_flagOSSF for data +# aliases['WH3l_flagOSSF'] = { +# 'linesToAdd' : [f'#include "{configurations}/ControlRegions/WZ/macros/flag_ossf.cc"'], +# 'class' : 'flag_ossf', +# 'args' : 'nLepton,Lepton_pt,Lepton_eta,Lepton_phi,Lepton_pdgId', +# 'samples' : ['DATA'], +# } + +# # WH3l_flagOSSF for data +# aliases['WH3l_chlll'] = { +# 'linesToAdd' : [f'#include "{configurations}/ControlRegions/WZ/macros/ch_lll.cc"'], +# 'class' : 'ch_lll', +# 'args' : 'nLepton,Lepton_pt,Lepton_eta,Lepton_phi,Lepton_pdgId', +# 'samples' : ['DATA'], +# } diff --git a/ControlRegions/WZ/2023_v12/configuration.py b/ControlRegions/WZ/2023_v12/configuration.py new file mode 100644 index 00000000..788b8cb5 --- /dev/null +++ b/ControlRegions/WZ/2023_v12/configuration.py @@ -0,0 +1,94 @@ +import sys,os + +# tag used to identify the configuration folder version +tag = "WZ2023_v12" + +# file to use as runner script, default uses mkShapesRDF.shapeAnalysis.runner, otherwise specify path to script +runnerFile = "default" + +# output file name +outputFile = "mkShapes__{}.root".format(tag) + +# path to ouput folder +outputFolder = "/eos/user/" + os.getlogin()[0] + "/" + os.getlogin() + "/mkShapesRDF_rootfiles/" + tag + "/rootFile/" + +# path to batch folder (used for condor submission) +batchFolder = "condor" + +# path to configuration folder (will contain all the compiled configuration files) +configsFolder = "configs" + +# luminosity to normalize to (in 1/fb) +# https://github.com/latinos/mkShapesRDF/blob/master/mkShapesRDF/processor/data/TrigMaker_cfg.py#L1151 +lumi = 17.794 + +# file with dict of aliases to define +aliasesFile = "aliases.py" + +# file with dict of variables +variablesFile = "variables.py" + +# file with dict of cuts +cutsFile = "cuts.py" + +# file with dict of samples +samplesFile = "samples.py" + +# file with dict of samples +plotFile = "plot.py" + +# file with dict of structure (used to define combine processes) +structureFile = "structure.py" + +# nuisances file for mkDatacards and for mkShape +nuisancesFile = "nuisances.py" + +# minRatio = 0.5 +# maxRatio = 1.5 + +# path to folder where to save plots +plotPath = "plots_" + tag + +# this lines are executed right before the runner on the condor node +mountEOS = [ + # "export KRB5CCNAME=/home/gpizzati/krb5\n", +] + +# list of imports to import when compiling the whole configuration folder, it should not contain imports used by configuration.py +imports = ["os", "glob", ("collections", "OrderedDict"), "ROOT"] + +# list of files to compile +filesToExec = [ + samplesFile, + aliasesFile, + variablesFile, + cutsFile, + plotFile, + nuisancesFile, + structureFile, +] + +# list of variables to keep in the compiled configuration folder +varsToKeep = [ + "batchVars", + "outputFolder", + "batchFolder", + "configsFolder", + "outputFile", + "runnerFile", + "tag", + "samples", + "aliases", + "variables", + ("cuts", {"cuts": "cuts", "preselections": "preselections"}), + ("plot", {"plot": "plot", "groupPlot": "groupPlot", "legend": "legend"}), + "nuisances", + "structure", + "lumi", +] + +# list of variables to keep in the batch submission script (script.py) +batchVars = varsToKeep[varsToKeep.index("samples") :] + + +varsToKeep += ['plotPath'] diff --git a/ControlRegions/WZ/2023_v12/cuts.py b/ControlRegions/WZ/2023_v12/cuts.py new file mode 100644 index 00000000..e271e33a --- /dev/null +++ b/ControlRegions/WZ/2023_v12/cuts.py @@ -0,0 +1,28 @@ +cuts = {} + +# Preselections - applied to all the cuts +preselections = 'Alt(Lepton_pt,0,0)>25 \ + && Alt(Lepton_pt,1,0)>10 \ + && (abs(Lepton_pdgId[1])==13 || Lepton_pt[1]>13) \ + && Alt(Lepton_pt,2,0)>10 \ + && (nLepton>=3 && Alt(Lepton_pt,3,0)<10) \ + && (WH3l_mOSll[0] < 0 || WH3l_mOSll[0] > 12) \ + && (WH3l_mOSll[1] < 0 || WH3l_mOSll[1] > 12) \ + && (WH3l_mOSll[2] < 0 || WH3l_mOSll[2] > 12) \ + && abs(WH3l_chlll) == 1 \ + && bVeto \ + && noJetInHorn \ +' + +# Inclusive +cuts['wh3l_wz_13TeV_incl'] = 'WH3l_flagOSSF == 1 && PuppiMET_pt > 45 && WH3l_ZVeto < 20 && WH3l_mlll > 100' + +# Jet bins +cuts['wh3l_wz_13TeV'] = { + 'expr' : 'WH3l_flagOSSF == 1 && PuppiMET_pt > 45 && WH3l_ZVeto < 20 && WH3l_mlll > 100', + 'categories' : { + '0j' : 'zeroJet', + '1j' : 'oneJet && Alt(CleanJet_pt,1,0)<30', + '2j' : 'multiJet', + } +} diff --git a/ControlRegions/WZ/2023_v12/nuisances.py b/ControlRegions/WZ/2023_v12/nuisances.py new file mode 100644 index 00000000..bd87d720 --- /dev/null +++ b/ControlRegions/WZ/2023_v12/nuisances.py @@ -0,0 +1,52 @@ +print(treeBaseDir) +def makeMCDirectory(var=''): + _treeBaseDir = treeBaseDir + '' + if useXROOTD: + _treeBaseDir = redirector + treeBaseDir + if var== '': + return '/'.join([_treeBaseDir, mcProduction, mcSteps]) + else: + return '/'.join([_treeBaseDir, mcProduction, mcSteps + '__' + var]) + + +# merge cuts +_mergedCuts = [] +for cut in list(cuts.keys()): + __cutExpr = '' + if type(cuts[cut]) == dict: + __cutExpr = cuts[cut]['expr'] + for cat in list(cuts[cut]['categories'].keys()): + _mergedCuts.append(cut + '_' + cat) + elif type(cuts[cut]) == str: + _mergedCuts.append(cut) + + +# Dfinitions of groups of samples +mc = [skey for skey in samples if skey not in ('DATA')] + +nuisances = {} + + +################################ EXPERIMENTAL UNCERTAINTIES ################################# + +#### Luminosity + +# https://twiki.cern.ch/twiki/bin/view/CMS/LumiRecommendationsRun3 +nuisances['lumi_2023'] = { + 'name' : 'lumi_2023', + 'type' : 'lnN', + 'samples' : dict((skey, '1.013') for skey in mc) +} + +### MC statistical uncertainty +autoStats = True +if autoStats: + ## Use the following if you want to apply the automatic combine MC stat nuisances. + nuisances['stat'] = { + 'type': 'auto', + 'maxPoiss': '10', + 'includeSignal': '0', + # nuisance ['maxPoiss'] = Number of threshold events for Poisson modelling + # nuisance ['includeSignal'] = Include MC stat nuisances on signal processes (1=True, 0=False) + 'samples': {} + } diff --git a/ControlRegions/WZ/2023_v12/plot.py b/ControlRegions/WZ/2023_v12/plot.py new file mode 100644 index 00000000..5490a0d9 --- /dev/null +++ b/ControlRegions/WZ/2023_v12/plot.py @@ -0,0 +1,79 @@ +# Group of plots + +groupPlot = {} + +groupPlot['DY'] = { + 'nameHR' : 'DY', + 'isSignal' : 0, + 'color' : 418, # kGreen + 2 + 'samples' : ['DY'] +} + +groupPlot['Top'] = { + 'nameHR' : 't#bar{t}', + 'isSignal' : 0, + 'color' : 400, # kYellow + 'samples' : ['Top'] +} + +groupPlot['WW'] = { + 'nameHR' : 'WW', + 'isSignal' : 0, + 'color' : 851, # kAzure -9 + 'samples' : ['WW'] +} + +groupPlot['WZ'] = { + 'nameHR' : 'WZ', + 'isSignal' : 0, + 'color' : 619, # kViolet + 1 + 'samples' : ['WZ'] +} + +# Plots + +plot = {} + +plot['DY'] = { + 'color' : 418, # kGreen + 2 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + +plot['Top'] = { + 'color' : 400, # kKYellow + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + +plot['WW'] = { + 'color' : 851, # kKYellow + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + +plot['WZ'] = { + 'color' : 619, # kKYellow + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + +# Data + +plot['DATA'] = { + 'nameHR' : 'Data', + 'color' : 1 , + 'isSignal' : 0, + 'isData' : 1 , + 'isBlind' : 0 +} + + +# Legend definition +legend = {} +legend['lumi'] = 'L = 17.8 fb^{-1}' +legend['sqrt'] = '#sqrt{s} = 13.6 TeV' diff --git a/ControlRegions/WZ/2023_v12/samples.py b/ControlRegions/WZ/2023_v12/samples.py new file mode 100644 index 00000000..421dfe11 --- /dev/null +++ b/ControlRegions/WZ/2023_v12/samples.py @@ -0,0 +1,201 @@ +from mkShapesRDF.lib.search_files import SearchFiles + +searchFiles = SearchFiles() + +redirector = "" +useXROOTD = False + +# MC: /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Summer23_130x_nAODv12_Full2023v12/MCl2loose2023v12__MCCorr2023v12JetScaling__l2tight +# DATA: /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2023_Prompt_nAODv12_Full2023v12/DATAl2loose2023v12__l2tight + +mcProduction = 'Summer23_130x_nAODv12_Full2023v12' +mcSteps = 'MCl2loose2023v12__MCCorr2023v12JetScaling__l2tight' +dataReco = 'Run2023_Prompt_nAODv12_Full2023v12' +dataSteps = 'DATAl2loose2023v12__l2tight' +# fakeSteps = 'DATAl1loose2022EFGv12__fakeW' + +############################################## +###### Tree base directory for the site ###### +############################################## +treeBaseDir = '/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano' +limitFiles = -1 + +def makeMCDirectory(var=""): + _treeBaseDir = treeBaseDir + "" + if redirector != "": + _treeBaseDir = redirector + treeBaseDir + if var == "": + return "/".join([_treeBaseDir, mcProduction, mcSteps]) + else: + return "/".join([_treeBaseDir, mcProduction, mcSteps + "__" + var]) + + +mcDirectory = makeMCDirectory() +# fakeDirectory = os.path.join(treeBaseDir, dataReco, fakeSteps) +dataDirectory = os.path.join(treeBaseDir, dataReco, dataSteps) + +samples = {} + + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + return [(name, _files[:limitFiles])] + else: + return [(name, _files)] + + +def CombineBaseW(samples, proc, samplelist): + _filtFiles = list(filter(lambda k: k[0] in samplelist, samples[proc]["name"])) + _files = list(map(lambda k: k[1], _filtFiles)) + _l = list(map(lambda k: len(k), _files)) + leastFiles = _files[_l.index(min(_l))] + dfSmall = ROOT.RDataFrame("Runs", leastFiles) + s = dfSmall.Sum("genEventSumw").GetValue() + f = ROOT.TFile(leastFiles[0]) + t = f.Get("Events") + t.GetEntry(1) + xs = t.baseW * s + + __files = [] + for f in _files: + __files += f + df = ROOT.RDataFrame("Runs", __files) + s = df.Sum("genEventSumw").GetValue() + newbaseW = str(xs / s) + weight = newbaseW + "/baseW" + + for iSample in samplelist: + addSampleWeight(samples, proc, iSample, weight) + + +def addSampleWeight(samples, sampleName, sampleNameType, weight): + obj = list(filter(lambda k: k[0] == sampleNameType, samples[sampleName]["name"]))[0] + samples[sampleName]["name"] = list( + filter(lambda k: k[0] != sampleNameType, samples[sampleName]["name"]) + ) + if len(obj) > 2: + samples[sampleName]["name"].append( + (obj[0], obj[1], obj[2] + "*(" + weight + ")") + ) + else: + samples[sampleName]["name"].append((obj[0], obj[1], "(" + weight + ")")) + + +################################################ +############ DATA DECLARATION ################## +################################################ + +DataRun = [ + ['Cv1','Run2023C-Prompt-v1'], + ['Cv2','Run2023C-Prompt-v2'], + ['Cv3','Run2023C-Prompt-v3'], + ['Cv4','Run2023C-Prompt-v4'], +] + + +DataSets = ['MuonEG','Muon0','Muon1','EGamma0','EGamma1'] + +DataTrig = { + 'MuonEG' : 'Trigger_ElMu' , + #'SingleMuon' : '!Trigger_ElMu && Trigger_sngMu' , + 'Muon0' : '!Trigger_ElMu && (Trigger_sngMu || Trigger_dblMu)', + 'Muon1' : '!Trigger_ElMu && (Trigger_sngMu || Trigger_dblMu)', + 'EGamma0' : '!Trigger_ElMu && !Trigger_sngMu && !Trigger_dblMu && (Trigger_sngEl || Trigger_dblEl)', + 'EGamma1' : '!Trigger_ElMu && !Trigger_sngMu && !Trigger_dblMu && (Trigger_sngEl || Trigger_dblEl)' +} + + + +#DataRun = [ +# ['C','Run2023C-Prompt-v1'], +# ['C','Run2023C-Prompt-v2'], +# ['C','Run2023C-Prompt-v3'], +# ['C','Run2023C-Prompt-v4'], +#] +# +#DataSets = ['MuonEG','Muon0','EGamma0'] +#DataSets = ['MuonEG','SingleMuon','Muon','EGamma'] +# +#DataTrig = { +# 'MuonEG' : ' Trigger_ElMu' , +# 'SingleMuon' : '!Trigger_ElMu && Trigger_sngMu' , +# 'Muon' : '!Trigger_ElMu && (Trigger_sngMu || Trigger_dblMu)', +# 'EGamma' : '!Trigger_ElMu && !Trigger_sngMu && !Trigger_dblMu && (Trigger_sngEl || Trigger_dblEl)' +#} + + +######################################### +############ MC COMMON ################## +######################################### + +# SFweight does not include btag weights +mcCommonWeightNoMatch = 'XSWeight*METFilter_Common*SFweight' +mcCommonWeight = 'XSWeight*METFilter_Common*PromptGenLepMatch3l*SFweight' + +#mcCommonWeight = 'XSWeight*METFilter_Common*SFweight' + +########################################### +############# BACKGROUNDS ############### +########################################### + +# DY +files = nanoGetSampleFiles(mcDirectory, 'DYto2L-2Jets_MLL-50') + +samples['DY'] = { + 'name': files, + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + +# Top +files = nanoGetSampleFiles(mcDirectory, 'TTTo2L2Nu') + +samples['Top'] = { + 'name': files, + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + +# WW +files = nanoGetSampleFiles(mcDirectory, 'WWTo2L2Nu') + +samples['WW'] = { + 'name': files, + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + +# WZ +files = nanoGetSampleFiles(mcDirectory, 'WZTo3LNu') + +samples['WZ'] = { + 'name': files, + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + +########################################### +################## DATA ################### +########################################### + +samples['DATA'] = { + 'name': [], + 'weight': 'LepWPCut*METFilter_DATA', + 'weights': [], + 'isData': ['all'], + 'FilesPerJob': 15 +} + +for _, sd in DataRun: + for pd in DataSets: + datatag = pd + '_' + sd + + if (pd == "SingleMuon" and _ in ["D"]) or (pd == "Muon" and _ == "B"): + continue + files = nanoGetSampleFiles(dataDirectory, datatag) + + print(datatag) + + samples['DATA']['name'].extend(files) + addSampleWeight(samples, 'DATA', datatag, DataTrig[pd]) diff --git a/ControlRegions/WZ/2023_v12/structure.py b/ControlRegions/WZ/2023_v12/structure.py new file mode 100644 index 00000000..ae0c2ae8 --- /dev/null +++ b/ControlRegions/WZ/2023_v12/structure.py @@ -0,0 +1,33 @@ +# structure configuration for datacard + +structure = {} + +# keys here must match keys in samples.py +# + +structure['DY'] = { + 'isSignal' : 0, + 'isData' : 0 +} + +structure['Top'] = { + 'isSignal' : 0, + 'isData' : 0 +} + +structure['WW'] = { + 'isSignal' : 0, + 'isData' : 0 +} + +structure['WZ'] = { + 'isSignal' : 0, + 'isData' : 0 +} + + +# data +structure['DATA'] = { + 'isSignal' : 0, + 'isData' : 1 +} diff --git a/ControlRegions/WZ/2023_v12/variables.py b/ControlRegions/WZ/2023_v12/variables.py new file mode 100644 index 00000000..bde2e953 --- /dev/null +++ b/ControlRegions/WZ/2023_v12/variables.py @@ -0,0 +1,158 @@ +# variables +variables = {} + +variables['events'] = { + 'name' : '1', + 'range' : (1,0,2), + 'xaxis' : 'events', + 'fold' : 3 +} + +variables['nvtx'] = { + 'name' : 'PV_npvsGood', + 'range' : (100, 0, 100), + 'xaxis' : 'number of vertices', + 'fold' : 3 +} + +variables['mll'] = { + 'name': 'mll', + 'range' : (60,60,120), + 'xaxis' : 'm_{ll} [GeV]', + 'fold' : 0 +} + +variables['mlll'] = { + 'name': 'WH3l_mlll', + 'range' : (50,0,200), + 'xaxis' : 'm_{lll} [GeV]', + 'fold' : 0 +} + +variables['ptll'] = { + 'name': 'ptll', + 'range' : (20, 0,200), + 'xaxis' : 'p_{T}^{ll} [GeV]', + 'fold' : 0 +} + +variables['drll'] = { + 'name': 'drll', + 'range' : (50, 0,5), + 'xaxis' : '#Delta R_{ll}', + 'fold' : 0 +} + +variables['dphill'] = { + 'name': 'dphill', + 'range' : (50, 0,5), + 'xaxis' : '#Delta #phi_{ll}', + 'fold' : 0 +} + +variables['ptll_more'] = { + 'name': 'ptll', + 'range' : (50, 0,100), + 'xaxis' : 'p_{T}^{ll} [GeV]', + 'fold' : 0 +} + +variables['pt1'] = { + 'name': 'Lepton_pt[0]', + 'range' : (20,0,200), + 'xaxis' : 'p_{T} 1st lep', + 'fold' : 3 +} + +variables['pt2'] = { + 'name': 'Lepton_pt[1]', + 'range' : (20,0,140), + 'xaxis' : 'p_{T} 2nd lep', + 'fold' : 3 +} + +variables['pt3'] = { + 'name': 'Lepton_pt[1]', + 'range' : (20,0,100), + 'xaxis' : 'p_{T} 3rd lep', + 'fold' : 3 +} + +variables['eta1'] = { + 'name': 'Lepton_eta[0]', + 'range' : (40,-3,3), + 'xaxis' : '#eta 1st lep', + 'fold' : 3 +} + +variables['eta2'] = { + 'name': 'Lepton_eta[1]', + 'range' : (40,-3,3), + 'xaxis' : '#eta 2nd lep', + 'fold' : 3 +} + +variables['eta3'] = { + 'name': 'Lepton_eta[2]', + 'range' : (40,-3,3), + 'xaxis' : '#eta 3rd lep', + 'fold' : 3 +} + +variables['puppimet'] = { + 'name': 'PuppiMET_pt', + 'range' : (20,0,200), + 'xaxis' : 'Puppi MET p_{T} [GeV]', + 'fold' : 3 +} + +############# New Jet processing + +variables['njet'] = { + 'name': 'Sum(CleanJet_pt>30)', + 'range' : (5,0,5), + 'xaxis' : 'Number of jets', + 'fold' : 2 +} + +variables['jetpt1'] = { + 'name': 'Alt(CleanJet_pt, 0, -99) - 9999.9*(CleanJet_pt[0]<30)', + 'range' : (40,0,200), + 'xaxis' : 'p_{T} 1st jet', + 'fold' : 0 +} + +variables['jetpt2'] = { + 'name': 'Alt(CleanJet_pt, 1, -99) - 9999.9*(CleanJet_pt[1]<30)', + 'range' : (40,0,200), + 'xaxis' : 'p_{T} 2nd jet', + 'fold' : 0 +} + +variables['jeteta1'] = { + 'name': 'Alt(CleanJet_eta, 0, -99) - 9999.9*(CleanJet_pt[0]<30)', + 'range' : (30,-4.7,4.7), + 'xaxis' : '#eta 1st jet', + 'fold' : 0 +} + +variables['jeteta1_fine_binning'] = { + 'name': 'Alt(CleanJet_eta, 0, -99) - 9999.9*(CleanJet_pt[0]<30)', + 'range' : (94,-4.7,4.7), + 'xaxis' : '#eta 1st jet', + 'fold' : 0 +} + +variables['jeteta2'] = { + 'name': 'Alt(CleanJet_eta, 1, -99) - 9999.9*(CleanJet_pt[1]<30)', + 'range' : (30,-4.7,4.7), + 'xaxis' : '#eta 2nd jet', + 'fold' : 0 +} + +variables['jeteta2_fine_binning'] = { + 'name': 'Alt(CleanJet_eta, 1, -99) - 9999.9*(CleanJet_pt[1]<30)', + 'range' : (94,-4.7,4.7), + 'xaxis' : '#eta 2nd jet', + 'fold' : 0 +} From 3448dd00c481b9a5c099244b7303ac895d04ac50 Mon Sep 17 00:00:00 2001 From: CodexForster Date: Mon, 17 Nov 2025 14:13:41 -0600 Subject: [PATCH 03/13] ZH3l run2 codes in RDF framework --- ZH3l_BDT/readme.md | 10 + ZH3l_BDT/zh3l_run2_rdf/aliases.py | 256 ++++++++ ZH3l_BDT/zh3l_run2_rdf/configuration.py | 92 +++ ZH3l_BDT/zh3l_run2_rdf/cuts.py | 139 +++++ ZH3l_BDT/zh3l_run2_rdf/macros/ch_lll.cc | 41 ++ ZH3l_BDT/zh3l_run2_rdf/macros/flag_ossf.cc | 57 ++ ZH3l_BDT/zh3l_run2_rdf/macros/mOS_ll.cc | 54 ++ ZH3l_BDT/zh3l_run2_rdf/macros/m_lll.cc | 49 ++ ZH3l_BDT/zh3l_run2_rdf/macros/z_veto.cc | 56 ++ ZH3l_BDT/zh3l_run2_rdf/ngenjet.cc | 121 ++++ ZH3l_BDT/zh3l_run2_rdf/nuisances.py | 580 ++++++++++++++++++ ZH3l_BDT/zh3l_run2_rdf/plot.py | 249 ++++++++ ZH3l_BDT/zh3l_run2_rdf/samples.py | 416 +++++++++++++ ZH3l_BDT/zh3l_run2_rdf/structure.py | 113 ++++ ZH3l_BDT/zh3l_run2_rdf/valid_data_samples.txt | 1 + ZH3l_BDT/zh3l_run2_rdf/variables.py | 129 ++++ 16 files changed, 2363 insertions(+) create mode 100644 ZH3l_BDT/readme.md create mode 100644 ZH3l_BDT/zh3l_run2_rdf/aliases.py create mode 100644 ZH3l_BDT/zh3l_run2_rdf/configuration.py create mode 100644 ZH3l_BDT/zh3l_run2_rdf/cuts.py create mode 100644 ZH3l_BDT/zh3l_run2_rdf/macros/ch_lll.cc create mode 100644 ZH3l_BDT/zh3l_run2_rdf/macros/flag_ossf.cc create mode 100644 ZH3l_BDT/zh3l_run2_rdf/macros/mOS_ll.cc create mode 100644 ZH3l_BDT/zh3l_run2_rdf/macros/m_lll.cc create mode 100644 ZH3l_BDT/zh3l_run2_rdf/macros/z_veto.cc create mode 100644 ZH3l_BDT/zh3l_run2_rdf/ngenjet.cc create mode 100644 ZH3l_BDT/zh3l_run2_rdf/nuisances.py create mode 100644 ZH3l_BDT/zh3l_run2_rdf/plot.py create mode 100644 ZH3l_BDT/zh3l_run2_rdf/samples.py create mode 100644 ZH3l_BDT/zh3l_run2_rdf/structure.py create mode 100644 ZH3l_BDT/zh3l_run2_rdf/valid_data_samples.txt create mode 100644 ZH3l_BDT/zh3l_run2_rdf/variables.py diff --git a/ZH3l_BDT/readme.md b/ZH3l_BDT/readme.md new file mode 100644 index 00000000..d4f7a5ab --- /dev/null +++ b/ZH3l_BDT/readme.md @@ -0,0 +1,10 @@ +Building towards a set of codes for the ZH3l analysis using the PlotsConfigurationsRun3/RDF framework. Among the many changes we desire to implement, the following are the ones we're starting out with: +1. Make the Run2 codes of ZH3l analysis compatible with the Run3 framework. (Relevant folder: zh3l_run2_rdf) +2. Write BDT codes in Run3 framework for Run2 analysis/data. (Relevant folder: zh3l_run2_bdt_rdf) +3. Setup a flow enabling a classification neural network in PlotsConfigurationsRun3 / mkShapesRDF + +Notes related to point 1 of the above list: +Most of the changes include switching to RDF compliant expressions, commands and functions. +Useful resource related to Run2->Run3 translation: https://indico.cern.ch/event/1201818/contributions/5180441/attachments/2567971/4427668/mkShapesRDF.pdf +Converting some functions in Run3 framework is pending and this has been put off for later. This means some nuisances (QCDscale and pdf) and base weights (for DY and ZZ samples) are not accurate. +10 out of 271 sample files were not processed owing to some segmentation faults (probably from corrupt/incorrect MC files). \ No newline at end of file diff --git a/ZH3l_BDT/zh3l_run2_rdf/aliases.py b/ZH3l_BDT/zh3l_run2_rdf/aliases.py new file mode 100644 index 00000000..0aeb3148 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/aliases.py @@ -0,0 +1,256 @@ +#Aliases (mostly btag) +import os +import copy +import inspect + +mc = [skey for skey in samples if skey not in ('Fake', 'DATA')] + +#2018 +#bWP = '0.1241' #Loose +bWP = '0.4184' +# DS - ideally from https://btv-wiki.docs.cern.ch/ScaleFactors/Run2UL2018/#general-remarks, but seems like this is calculated from another dataset: https://indico.cern.ch/event/826738/contributions/3464989/attachments/1860911/3058344/BTag_190612_PtRel.pdf. +configurations = os.path.realpath(inspect.getfile(inspect.currentframe())) # this file +configurations = os.path.dirname(configurations) # 2022_v12 +configurations = os.path.dirname(configurations) # WZ +configurations = os.path.dirname(configurations) # ControlRegions +configurations = os.path.dirname(configurations) # PlotsConfigurationsRun3 + +aliases = {} +aliases = OrderedDict() + +aliases['bVeto'] = { + 'expr': '(Sum((CleanJet_pt > 20.0 && abs(CleanJet_eta) < 2.5) * Take(Jet_btagDeepB, CleanJet_jetIdx) > ' + bWP + ') == 0)' + +} + +aliases['btagSF'] = { +'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>20 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_deepcsv_shape, CleanJet_jetIdx)+1*(CleanJet_pt<20 || abs(CleanJet_eta)>2.5))))', +'samples': mc +} + +systs = ['jes','lf','hf','lfstats1','lfstats2','hfstats1','hfstats2','cferr1','cferr2'] +# DS -(?)- jes - Jet Energy Scale uncertainty, lf/hf - light/heavy flavor, stats - statistical uncertainty, cferr - charm flavour + +for s in systs: + aliases['btagSF'+s+'up'] = { + 'expr': aliases['btagSF']['expr'].replace('shape','shape_up_'+s), + 'samples':mc + } + aliases['btagSF'+s+'down'] = { + 'expr': aliases['btagSF']['expr'].replace('shape','shape_down_'+s), + 'samples':mc + } + +aliases['Jet_PUIDSF'] = { + 'expr': 'TMath::Exp(Sum((Jet_jetId >= 2) * LogVec(Jet_PUIDSF_loose)))', + 'samples': mc +} + +aliases['Jet_PUIDSF_up'] = { + 'expr': 'TMath::Exp(Sum((Jet_jetId >= 2) * LogVec(Jet_PUIDSF_loose_up)))', + 'samples': mc +} + +aliases['Jet_PUIDSF_down'] = { + 'expr': 'TMath::Exp(Sum((Jet_jetId >= 2) * LogVec(Jet_PUIDSF_loose_down)))', + 'samples': mc +} + +aliases['PromptGenLepMatch3l'] = { + 'expr': 'Alt(Lepton_promptgenmatched, 0, 0) * Alt(Lepton_promptgenmatched, 1, 0) * Alt(Lepton_promptgenmatched, 2, 0)', + 'samples': mc +} + +#aliases['LepWPCutNew'] = { 'expr': '(((abs(Lepton_pdgId[0])==13 && Muon_mvaTTH[Lepton_muonIdx[0]]>0.8) || (abs(Lepton_pdgId[0])==11 && Electron_mvaTTH[Lepton_electronIdx[0]]>0.7)) && ((abs(Lepton_pdgId[1])==13 && Muon_mvaTTH[Lepton_muonIdx[1]]>0.8) || (abs(Lepton_pdgId[1])==11 && Electron_mvaTTH[Lepton_electronIdx[1]]>0.7)) && ((abs(Lepton_pdgId[2])==13 && Muon_mvaTTH[Lepton_muonIdx[2]]>0.8) || (abs(Lepton_pdgId[2])==11 && Electron_mvaTTH[Lepton_electronIdx[2]]>0.7)))', + # 'samples': mc + ['DATA'] +#} + +aliases['Top_pTrw'] = { + 'expr': '(topGenPt * antitopGenPt > 0.) * (TMath::Sqrt(TMath::Exp(0.0615 - 0.0005 * topGenPt) * TMath::Exp(0.0615 - 0.0005 * antitopGenPt))) + (topGenPt * antitopGenPt <= 0.)', + 'samples': ['top'] +} + +#aliases['ZH3l_dphilmetjj_test'] = { +# 'linesToAdd': [ +# '.L %s/src/PlotsConfigurations/Configurations/ZH3l/scripts/ZH3l_patch.cc+' % os.getenv('CMSSW_BASE') +# ], +# 'class': 'ZH3l_patch', +# 'args': ("dphilmetjj") +#} + +#aliases['ZH3l_dphilmetj_test'] = { +# 'class': 'ZH3l_patch', +# 'args': ("dphilmetj") +#} + +#aliases['ZH3l_mTlmet_test'] = { +# 'class': 'ZH3l_patch', +# 'args': ("mTlmet") +#} + +#aliases['ZH3l_mTlmetj_test'] = { +# 'class': 'ZH3l_patch', +# 'args': ("mTlmetj") +#} + +#aliases['ZH3l_mTlmetjj_test'] = { +# 'class': 'ZH3l_patch', +# 'args': ("mTlmetjj") +#} + +####################### +### SFs for tthMVA ### +####################### + +aliases['SFweightEleUp'] = { + 'expr': 'LepSF3l__ele_'+eleWP_new+'__Up', + 'samples': mc +} + +aliases['SFweightEleDown'] = { + 'expr': 'LepSF3l__ele_'+eleWP_new+'__Do', + 'samples': mc +} + +aliases['SFweightMuUp'] = { + 'expr': 'LepSF3l__mu_'+muWP_new+'__Up', + 'samples': mc +} + +aliases['SFweightMuDown'] = { + 'expr': 'LepSF3l__mu_'+muWP_new+'__Do', + 'samples': mc +} + + +#aliases['ttHMVA_SF_3l'] = { +# 'linesToAdd': ['.L %s/src/PlotsConfigurations/Configurations/patches/compute_SF_BETA.C+' % os.getenv('CMSSW_BASE')], +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'total_SF'), +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Up_0'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_up', 0), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Up_1'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_up', 1), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Up_2'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_up', 2), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Down_0'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_down', 0), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Down_1'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_down', 1), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Down_2'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_down', 2), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_3l_ele_SF_Up'] = { +# 'expr' : '(ttHMVA_SF_Up_0[0]*(abs(Lepton_pdgId[0]) == 11) + (abs(Lepton_pdgId[0]) == 13)) *\ +# (ttHMVA_SF_Up_1[0]*(abs(Lepton_pdgId[1]) == 11) + (abs(Lepton_pdgId[1]) == 13)) *\ +# (ttHMVA_SF_Up_2[0]*(abs(Lepton_pdgId[2]) == 11) + (abs(Lepton_pdgId[2]) == 13))', +# 'nominalOnly' : True, +# 'samples' : mc +#} + +#aliases['ttHMVA_3l_ele_SF_Down'] = { +# 'expr' : '(ttHMVA_SF_Down_0[0]*(abs(Lepton_pdgId[0]) == 11) + (abs(Lepton_pdgId[0]) == 13)) *\ +# (ttHMVA_SF_Down_1[0]*(abs(Lepton_pdgId[1]) == 11) + (abs(Lepton_pdgId[1]) == 13)) *\ +# (ttHMVA_SF_Down_2[0]*(abs(Lepton_pdgId[2]) == 11) + (abs(Lepton_pdgId[2]) == 13))', +# 'nominalOnly' : True, +# 'samples' : mc +#} + +#aliases['ttHMVA_3l_mu_SF_Up'] = { +# 'expr' : '(ttHMVA_SF_Up_0[0]*(abs(Lepton_pdgId[0]) == 13) + (abs(Lepton_pdgId[0]) == 11)) *\ +# (ttHMVA_SF_Up_1[0]*(abs(Lepton_pdgId[1]) == 13) + (abs(Lepton_pdgId[1]) == 11)) *\ +# (ttHMVA_SF_Up_2[0]*(abs(Lepton_pdgId[2]) == 13) + (abs(Lepton_pdgId[2]) == 11))', +# 'nominalOnly' : True, +# 'samples' : mc +#} + +#aliases['ttHMVA_3l_mu_SF_Down'] = { +# 'expr' : '(ttHMVA_SF_Down_0[0]*(abs(Lepton_pdgId[0]) == 13) + (abs(Lepton_pdgId[0]) == 11)) *\ +# (ttHMVA_SF_Down_1[0]*(abs(Lepton_pdgId[1]) == 13) + (abs(Lepton_pdgId[1]) == 11)) *\ +# (ttHMVA_SF_Down_2[0]*(abs(Lepton_pdgId[2]) == 13) + (abs(Lepton_pdgId[2]) == 11))', +# 'nominalOnly' : True, +# 'samples' : mc +#} + +# In WpWmJJ_EWK events, partons [0] and [1] are always the decay products of the first W +aliases['lhe_mW1'] = { + 'expr': 'TMath::Sqrt(2. * Take(LHEPart_pt, 0) * Take(LHEPart_pt, 1) * (TMath::CosH(Take(LHEPart_eta, 0) - Take(LHEPart_eta, 1)) - TMath::Cos(Take(LHEPart_phi, 0) - Take(LHEPart_phi, 1))))', + 'samples': ['WWewk'] +} + +# and [2] [3] are the second W +aliases['lhe_mW2'] = { + 'expr': 'TMath::Sqrt(2. * Take(LHEPart_pt, 2) * Take(LHEPart_pt, 3) * (TMath::CosH(Take(LHEPart_eta, 2) - Take(LHEPart_eta, 3)) - TMath::Cos(Take(LHEPart_phi, 2) - Take(LHEPart_phi, 3))))', + 'samples': ['WWewk'] +} + +aliases['gstarHigh'] = { + 'expr': 'Gen_ZGstar_mass <0 || Gen_ZGstar_mass > 4', + 'samples': ['WZ'] +} + +# aliases['nCleanGenJet'] = { +# 'linesToAdd': ['.L %s/src/PlotsConfigurations/Configurations/Differential/ngenjet.cc+' % os.getenv('CMSSW_BASE') +# ], +# 'class': 'CountGenJet', +# 'samples': mc +# } + +# configurations = os.path.abspath('.') + '/' +# print(configurations) +# print('\n\n\n') + +# aliases['nCleanGenJet'] = { +# 'linesToAdd': ['.L %sngenjet.cc+' % configurations], +# 'class': 'CountGenJet', +# 'args': 'nLeptonGen, LeptonGen_isPrompt,\ +# LeptonGen_pdgId, LeptonGen_pt, LeptonGen_eta, LeptonGen_phi, \ +# LeptonGen_mass, nPhotonGen, PhotonGen_pt, PhotonGen_eta,PhotonGen_phi, \ +# PhotonGen_mass, nGenJet, GenJet_pt, GenJet_eta, GenJet_phi', +# 'samples': mc +# } + +#puidSFSource = '%s/src/LatinoAnalysis/NanoGardener/python/data/JetPUID_effcyandSF.root' % os.getenv('CMSSW_BASE') +#puidSFSource = '%s/src/PlotsConfigurations/Configurations/patches/PUID_80XTraining_EffSFandUncties.root' % os.getenv('CMSSW_BASE') + +#aliases['PUJetIdSF'] = { +# 'linesToAdd': [ +# 'gSystem->AddIncludePath("-I%s/src");' % os.getenv('CMSSW_BASE'), +# '.L %s/src/PlotsConfigurations/Configurations/patches/pujetidsf_event_new.cc+' % os.getenv('CMSSW_BASE') +# ], +# 'class': 'PUJetIdEventSF', +# 'args': (puidSFSource, '2018', 'loose'), +# 'samples': mc +#} diff --git a/ZH3l_BDT/zh3l_run2_rdf/configuration.py b/ZH3l_BDT/zh3l_run2_rdf/configuration.py new file mode 100644 index 00000000..de531ef0 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/configuration.py @@ -0,0 +1,92 @@ +import sys,os + +# tag used to identify the configuration folder version +tag = 'ZH3l_BDTrun2' + +# file to use as runner script, default uses mkShapesRDF.shapeAnalysis.runner, otherwise specify path to script +runnerFile = "default" + +# output file name +outputFile = "mkShapes__{}.root".format(tag) + +# path to ouput folder +outputFolder = "/eos/user/" + os.getlogin()[0] + "/" + os.getlogin() + "/mkShapesRDF_rootfiles/" + tag + "/rootFile/" + +# path to batch folder (used for condor submission) +batchFolder = "condor" + +# path to configuration folder (will contain all the compiled configuration files) +configsFolder = "configs" + +# luminosity to normalize to (in 1/fb) +# https://github.com/latinos/mkShapesRDF/blob/Run3/mkShapesRDF/processor/data/TrigMaker_cfg.py#L1016 +lumi = 8.174732641 + +# file with TTree aliases +aliasesFile = 'aliases.py' + +# file with list of variables +variablesFile = 'variables.py' + +# file with list of cuts +cutsFile = 'cuts.py' + +# file with list of samples +samplesFile = 'samples.py' + +# file with list of samples +plotFile = 'plot.py' + +# structure file for datacard +structureFile = 'structure.py' + +# nuisances file for mkDatacards and for mkShape +nuisancesFile = 'nuisances.py' + +# path to folder where to save plots +plotPath = "plots_" + tag + +# this lines are executed right before the runner on the condor node +mountEOS = [ + # "export KRB5CCNAME=/home/gpizzati/krb5\n", +] + +# list of imports to import when compiling the whole configuration folder, it should not contain imports used by configuration.py +imports = ["os", "glob", ("collections", "OrderedDict"), "ROOT"] + +# list of files to compile +filesToExec = [ + samplesFile, + aliasesFile, + cutsFile, + variablesFile, + plotFile, + nuisancesFile, + structureFile, +] + +# list of variables to keep in the compiled configuration folder +varsToKeep = [ + "batchVars", + "outputFolder", + "batchFolder", + "configsFolder", + "outputFile", + "runnerFile", + "tag", + "samples", + "aliases", + "variables", + ("cuts", {"cuts": "cuts", "preselections": "preselections"}), + ("plot", {"plot": "plot", "groupPlot": "groupPlot", "legend": "legend"}), + "nuisances", + "structure", + "lumi", +] + +# list of variables to keep in the batch submission script (script.py) +batchVars = varsToKeep[varsToKeep.index("samples") :] + + +varsToKeep += ['plotPath'] + diff --git a/ZH3l_BDT/zh3l_run2_rdf/cuts.py b/ZH3l_BDT/zh3l_run2_rdf/cuts.py new file mode 100644 index 00000000..a620ab34 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/cuts.py @@ -0,0 +1,139 @@ +cuts = {} + +preselections = 'Alt( Lepton_pt, 0, 0)>25 \ + && Alt( Lepton_pt, 1, 0)>20 \ + && Alt( Lepton_pt, 2, 0)>15 \ + && Alt( Lepton_pt, 3, 0)<10 \ + && (WH3l_mOSll[0] < 0 || WH3l_mOSll[0] > 12) \ + && (WH3l_mOSll[1] < 0 || WH3l_mOSll[1] > 12) \ + && (WH3l_mOSll[2] < 0 || WH3l_mOSll[2] > 12) \ + && abs(WH3l_chlll) == 1 \ + ' + +cuts['zmass_cut'] = 'WH3l_ZVeto < 25' + +cuts['jet_cut_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + ' + +cuts['bveto_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + ' + +cuts['z4lveto_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + ' + +cuts['zh3l_SR_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetjj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetjj > 3.14159/2 \ + ' + +cuts['met_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + ' + +cuts['zh3l_SR_2j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetjj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_2j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetjj > 3.14159/2 \ + ' + +cuts['jet_cut_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + ' + +cuts['bveto_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + ' + +cuts['z4lveto_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + ' + +cuts['zh3l_SR_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetj > 3.14159/2 \ + ' + +cuts['met_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + ' + +cuts['zh3l_SR_1j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_1j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetj > 3.14159/2 \ + ' + +''' + #11 = e +# 13 = mu +# 15 = tau +''' diff --git a/ZH3l_BDT/zh3l_run2_rdf/macros/ch_lll.cc b/ZH3l_BDT/zh3l_run2_rdf/macros/ch_lll.cc new file mode 100644 index 00000000..de6ef009 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/macros/ch_lll.cc @@ -0,0 +1,41 @@ +#ifndef whchlll +#define whchlll + + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" +#include "TLorentzVector.h" + +using namespace ROOT; +using namespace ROOT::VecOps; + +float ch_lll(int nLepton, + RVecF Lepton_pt, + RVecF Lepton_eta, + RVecF Lepton_phi, + RVecF Lepton_pdgId){ + + + // Create default value + float ch_lll = -9999.0; + + // Check that we have at least 3 good leptons + if (nLepton < 3) return ch_lll; + + bool WH3l_ok = abs( abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2] ) <= 1; + if (!WH3l_ok) return ch_lll; + + ch_lll = abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2]; + + return ch_lll; +} + + +#endif diff --git a/ZH3l_BDT/zh3l_run2_rdf/macros/flag_ossf.cc b/ZH3l_BDT/zh3l_run2_rdf/macros/flag_ossf.cc new file mode 100644 index 00000000..a46acea2 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/macros/flag_ossf.cc @@ -0,0 +1,57 @@ +#ifndef flagossf +#define flagossf + + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" +#include "TLorentzVector.h" + +using namespace ROOT; +using namespace ROOT::VecOps; + +bool flag_ossf(int nLepton, + RVecF Lepton_pt, + RVecF Lepton_eta, + RVecF Lepton_phi, + RVecF Lepton_pdgId){ + + // Create default value + bool flag_OSSF = false; + + // Check that we have at least 3 good leptons + if (nLepton < 3) return flag_OSSF; + + bool WH3l_ok = abs( abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2] ) <= 1; + if (!WH3l_ok) return flag_OSSF; + + // Initialize leptons 4-vectors + std::vector leptons_vector = { + ROOT::Math::PtEtaPhiMVector(Lepton_pt[0],Lepton_eta[0],Lepton_phi[0],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[1],Lepton_eta[1],Lepton_phi[1],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[2],Lepton_eta[2],Lepton_phi[2],0) + }; + + // Compute minimum difference |mll - mZ| + float minmllDiffToZ = 9999.0; + for (uint i = 0; i < 3; i++){ + for (uint j = i+1; j < 3; j++){ + if ( Lepton_pdgId[i] + Lepton_pdgId[j] != 0 ) continue; + float mllDiffToZ = abs( (leptons_vector[i] + leptons_vector[j]).M() - 91.1876 ); + if ( mllDiffToZ < minmllDiffToZ ) minmllDiffToZ = mllDiffToZ; + } + } + + if (minmllDiffToZ != 9999.0) flag_OSSF = true; + + return flag_OSSF; +} + + +#endif diff --git a/ZH3l_BDT/zh3l_run2_rdf/macros/mOS_ll.cc b/ZH3l_BDT/zh3l_run2_rdf/macros/mOS_ll.cc new file mode 100644 index 00000000..1ec3c638 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/macros/mOS_ll.cc @@ -0,0 +1,54 @@ +#ifndef mosll +#define mosll + + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" +#include "TLorentzVector.h" + +using namespace ROOT; +using namespace ROOT::VecOps; + +ROOT::RVecF mOS_ll(int nLepton, + RVecF Lepton_pt, + RVecF Lepton_eta, + RVecF Lepton_phi, + RVecF Lepton_pdgId){ + + // Create default value + ROOT::RVecF mOSll_vector = {-9999.0, -9999.0, -9999.0}; + + // Check that we have at least 3 good leptons + if (nLepton < 3) return mOSll_vector; + + bool WH3l_ok = abs( abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2] ) <= 1; + if (!WH3l_ok) return mOSll_vector; + + // Initialize leptons 4-vectors + std::vector leptons_vector = { + ROOT::Math::PtEtaPhiMVector(Lepton_pt[0],Lepton_eta[0],Lepton_phi[0],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[1],Lepton_eta[1],Lepton_phi[1],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[2],Lepton_eta[2],Lepton_phi[2],0) + }; + + // Loop over the three leptons + for (uint i = 0; i < 3; i++){ + for (uint j = i+1; j < 3; j++){ + if (Lepton_pdgId[i]*Lepton_pdgId[j] < 0) + mOSll_vector.push_back( (leptons_vector[i]+leptons_vector[j]).M() ); + else + mOSll_vector.push_back(-9999.0); + } + } + return mOSll_vector; +} + + +#endif diff --git a/ZH3l_BDT/zh3l_run2_rdf/macros/m_lll.cc b/ZH3l_BDT/zh3l_run2_rdf/macros/m_lll.cc new file mode 100644 index 00000000..46ce151b --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/macros/m_lll.cc @@ -0,0 +1,49 @@ +#ifndef whmlll +#define whmlll + + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" +#include "TLorentzVector.h" + +using namespace ROOT; +using namespace ROOT::VecOps; + +float m_lll(int nLepton, + RVecF Lepton_pt, + RVecF Lepton_eta, + RVecF Lepton_phi, + RVecF Lepton_pdgId){ + + + // Create default value + float mlll = -9999.0; + + // Check that we have at least 3 good leptons + if (nLepton < 3) return mlll; + + bool WH3l_ok = abs( abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2] ) <= 1; + if (!WH3l_ok) return mlll; + + // Initialize leptons 4-vectors + std::vector leptons_vector = { + ROOT::Math::PtEtaPhiMVector(Lepton_pt[0],Lepton_eta[0],Lepton_phi[0],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[1],Lepton_eta[1],Lepton_phi[1],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[2],Lepton_eta[2],Lepton_phi[2],0) + }; + + mlll = (leptons_vector[0] + leptons_vector[1] + leptons_vector[2]).M(); + + return mlll; + +} + + +#endif diff --git a/ZH3l_BDT/zh3l_run2_rdf/macros/z_veto.cc b/ZH3l_BDT/zh3l_run2_rdf/macros/z_veto.cc new file mode 100644 index 00000000..a527c0c4 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/macros/z_veto.cc @@ -0,0 +1,56 @@ +#ifndef zveto +#define zveto + + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" +#include "TLorentzVector.h" + +using namespace ROOT; +using namespace ROOT::VecOps; + +float z_veto(int nLepton, + RVecF Lepton_pt, + RVecF Lepton_eta, + RVecF Lepton_phi, + RVecF Lepton_pdgId){ + + + // Create default value + float z_veto = 9999.0; + + // Check that we have at least 3 good leptons + if (nLepton < 3) return z_veto; + + bool WH3l_ok = abs( abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2] ) <= 1; + if (!WH3l_ok) return z_veto; + + // Initialize leptons 4-vectors + std::vector leptons_vector = { + ROOT::Math::PtEtaPhiMVector(Lepton_pt[0],Lepton_eta[0],Lepton_phi[0],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[1],Lepton_eta[1],Lepton_phi[1],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[2],Lepton_eta[2],Lepton_phi[2],0) + }; + + // Compute minimum difference |mll - mZ| + float minmllDiffToZ = 9999.0; + for (uint i = 0; i < 3; i++){ + for (uint j = i+1; j < 3; j++){ + if ( Lepton_pdgId[i] + Lepton_pdgId[j] != 0 ) continue; + float mllDiffToZ = abs( (leptons_vector[i] + leptons_vector[j]).M() - 91.1876 ); + if ( mllDiffToZ < minmllDiffToZ ) minmllDiffToZ = mllDiffToZ; + } + } + + return minmllDiffToZ; +} + + +#endif diff --git a/ZH3l_BDT/zh3l_run2_rdf/ngenjet.cc b/ZH3l_BDT/zh3l_run2_rdf/ngenjet.cc new file mode 100644 index 00000000..99893569 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/ngenjet.cc @@ -0,0 +1,121 @@ + +#ifndef ngenjet +#define ngenjet + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" + +using namespace ROOT; +using namespace ROOT::VecOps; +double CountGenJet( + int nLeptonGen, + RVecB LeptonGen_isPrompt, + RVecI LeptonGen_pdgId, + RVecF LeptonGen_pt, + RVecF LeptonGen_eta, + RVecF LeptonGen_phi, + RVecF LeptonGen_mass, + int nPhotonGen, + RVecF PhotonGen_pt, + RVecF PhotonGen_eta, + RVecF PhotonGen_phi, + RVecF PhotonGen_mass, + int nGenJet, + RVecF GenJet_pt, + RVecF GenJet_eta, + RVecF GenJet_phi + ){ + unsigned nJ = nGenJet; + + unsigned nL = nLeptonGen; + + std::vector iPromptL{}; + iPromptL.reserve(nL); + + for (unsigned iL{0}; iL != nL; ++iL) { + if (!LeptonGen_isPrompt[iL]) + continue; + + unsigned absId{static_cast(std::abs(LeptonGen_pdgId[iL]))}; + if (absId != 11 && absId != 13) + continue; + + iPromptL.push_back(iL); + } + + if (iPromptL.size() == 0) { + unsigned n{0}; + for (unsigned iJ{0}; iJ != nJ; ++iJ) { + if (GenJet_pt[iJ] > 30.) + ++n; + } + return n; + } + + std::vector dressedLeptons{}; + for (unsigned iL : iPromptL) { + dressedLeptons.push_back( + ROOT::Math::PtEtaPhiMVector( + LeptonGen_pt[iL], + LeptonGen_eta[iL], + LeptonGen_phi[iL], + LeptonGen_mass[iL] + ) + ); + } + + unsigned nP = nPhotonGen; + + for (unsigned iP{0}; iP != nP; ++iP) { + double minDR2{1000.}; + int iDMin{-1}; + for (unsigned iD{0}; iD != iPromptL.size(); ++iD) { + unsigned iL{iPromptL[iD]}; + double dEta{LeptonGen_eta[iL] - PhotonGen_eta[iP]}; + double dPhi{TVector2::Phi_mpi_pi(LeptonGen_phi[iL] - PhotonGen_phi[iP])}; + double dR2{dEta * dEta + dPhi * dPhi}; + if (dR2 < minDR2) { + minDR2 = dR2; + iDMin = iD; + } + } + + if (minDR2 < 0.09) + dressedLeptons[iDMin] += ROOT::Math::PtEtaPhiMVector( + PhotonGen_pt[iP], + PhotonGen_eta[iP], + PhotonGen_phi[iP], + PhotonGen_mass[iP]); + } + + unsigned n{0}; + for (unsigned iJ{0}; iJ != nJ; ++iJ) { + if (GenJet_pt[iJ] < 30.) + continue; + + bool overlap{false}; + for (auto& p4 : dressedLeptons) { + if (p4.pt() < 10.) + continue; + + double dEta{p4.eta() - GenJet_eta[iJ]}; + double dPhi{TVector2::Phi_mpi_pi(p4.phi() - GenJet_phi[iJ])}; + if (dEta * dEta + dPhi * dPhi < 0.016) { + overlap = true; + break; + } + } + if (!overlap) + ++n; + } + return n; +} + +#endif \ No newline at end of file diff --git a/ZH3l_BDT/zh3l_run2_rdf/nuisances.py b/ZH3l_BDT/zh3l_run2_rdf/nuisances.py new file mode 100644 index 00000000..2dc2d2b2 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/nuisances.py @@ -0,0 +1,580 @@ +# nuisances +# name of samples here must match keys in samples.py + +try: + mc = [skey for skey in samples if skey != 'DATA' and not skey.startswith('Fake')] +except NameError: + mc = [] + +try: + fitcuts = [cut for cut in cuts if 'SR' in cut or 'CR' in cut] +except NameError: + fitcuts = [] + +nuisances = {} + +#### Luminosity + +nuisances['lumi_Uncorrelated'] = { + 'name': 'lumi_13TeV_2018', + 'type': 'lnN', + 'samples': dict((skey, '1.015') for skey in mc if skey not in ['WZ']), + 'cuts' : fitcuts +} + +nuisances['lumi_XYFact'] = { + 'name': 'lumi_13TeV_XYFact', + 'type': 'lnN', + 'samples': dict((skey, '1.02') for skey in mc if skey not in ['WZ']), + 'cuts' : fitcuts +} + +nuisances['lumi_LScale'] = { + 'name': 'lumi_13TeV_LSCale', + 'type': 'lnN', + 'samples': dict((skey, '1.002') for skey in mc if skey not in ['WZ']), + 'cuts' : fitcuts +} + +nuisances['lumi_CurrCalib'] = { + 'name': 'lumi_13TeV_CurrCalib', + 'type': 'lnN', + 'samples': dict((skey, '1.002') for skey in mc if skey not in ['WZ']), + 'cuts' : fitcuts +} + +#### Theoretical Systematics +# Commenting out as mkShapesRDF does not have LatinoAnalysis tools' HiggsXSection module (DS, 03Nov25). +# Scale +# from LatinoAnalysis.Tools.HiggsXSection import * +# HiggsXS = HiggsXSection() + +# nuisances['QCDscale_VH'] = { +# 'name' : 'QCDscale_VH', +# 'samples' : { +# 'WH_hww' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','WH','125.09','scale','sm'), +# 'ZH_hww' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ZH','125.09','scale','sm'), +# 'WH_htt' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','WH','125.09','scale','sm'), +# 'ZH_htt' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ZH','125.09','scale','sm') +# }, +# 'type' : 'lnN', +# 'cuts' : fitcuts +# } + +# nuisances['QCDscale_ggZH'] = { +# 'name' : 'QCDscale_ggZH', +# 'samples' : { +# 'ggZH_hww': HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ggZH','125.09','scale','sm'), +# }, +# 'type' : 'lnN', +# 'cuts' : fitcuts +# } + +# nuisances['QCDscale_ttH'] = { +# 'name' : 'QCDscale_ttH', +# 'samples' : { +# 'ttH_hww': HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ttH','125.09','scale','sm'), +# }, +# 'type' : 'lnN', +# 'cuts' : fitcuts +# } + +variations = ['Alt(LHEScaleWeight, 0, 1)', 'Alt(LHEScaleWeight, 1, 1)', 'Alt(LHEScaleWeight, 3, 1)', 'Alt(LHEScaleWeight, 5, 1)', 'Alt(LHEScaleWeight, 7, 1)', 'Alt(LHEScaleWeight, 8, 1)'] +# variations = ['Alt$(LHEScaleWeight[0],1)', 'Alt$(LHEScaleWeight[1],1)', 'Alt$(LHEScaleWeight[3],1)', 'Alt$(LHEScaleWeight[5],1)', 'Alt$(LHEScaleWeight[7],1)', 'Alt$(LHEScaleWeight[8],1)'] + +# Commenting out as mkShapesRDF could not find variations for the QCDscale nuisances (DS, 05Nov25). +# nuisances['QCDscale_V'] = { +# 'name': 'QCDscale_V', +# 'kind': 'weight_envelope', +# 'type': 'shape', +# 'samples': {'DY': variations}, +# 'AsLnN': '1', +# 'cuts' : fitcuts +# } + +# nuisances['QCDscale_VV'] = { +# 'name': 'QCDscale_VV', +# 'kind': 'weight_envelope', +# 'type': 'shape', +# 'samples': { +# 'WW' : variations, +# 'Zg' : variations, +# 'ZgS' : variations, +# 'WZ' : variations, +# 'ZZ' : variations +# }, +# 'cuts' : fitcuts +# } + +nuisances['QCDscale_WWewk'] = { + 'name': 'QCDscale_WWewk', + 'samples': { + 'WWewk': '1.11', + }, + 'type': 'lnN', + 'cuts' : fitcuts +} + + +# ggww and interference +nuisances['QCDscale_ggVV'] = { + 'name': 'QCDscale_ggVV', + 'type': 'lnN', + 'samples': { + 'ggWW': '1.15', + }, + 'cuts' : fitcuts +} + +nuisances['QCDscale_qqbar_ACCEPT'] = { + 'name' : 'QCDscale_qqbar_ACCEPT', + 'type' : 'lnN', + 'samples' : { + 'WH_hww' : '1.010', + 'ZH_hww' : '1.015', + 'WH_htt' : '1.010', + 'ZH_htt' : '1.015', + }, + 'cuts' : fitcuts +} + +nuisances['QCDscale_gg_ACCEPT'] = { + 'name' : 'QCDscale_gg_ACCEPT', + 'samples' : { + 'ggZH_hww': '1.012', + 'ggWW' : '1.012' + }, + 'type' : 'lnN', + 'cuts' : fitcuts +} + +# Commenting out as mkShapesRDF does not have LatinoAnalysis tools' HiggsXSection module (DS, 03Nov25). +# pdf +# nuisances['pdf_Higgs_gg'] = { +# 'name' : 'pdf_Higgs_gg', +# 'samples' : { +# 'ggZH_hww': HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ggZH','125.09','pdf','sm'), +# }, +# 'type' : 'lnN', +# 'cuts' : fitcuts +# } + +# nuisances['pdf_Higgs_qqbar'] = { +# 'name' : 'pdf_Higgs_qqbar', +# 'type' : 'lnN', +# 'samples' : { +# 'WH_hww' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','WH' ,'125.09','pdf','sm'), +# 'ZH_hww' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ZH' ,'125.09','pdf','sm'), +# 'WH_htt' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','WH' ,'125.09','pdf','sm'), +# 'ZH_htt' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ZH' ,'125.09','pdf','sm'), +# }, +# 'cuts' : fitcuts +# } + +# nuisances['pdf_Higgs_ttH'] = { +# 'name': 'pdf_Higgs_ttH', +# 'type': 'lnN', +# 'samples': { +# 'ttH_hww' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ttH','125.09','pdf','sm') +# }, +# 'cuts' : fitcuts +# } + +nuisances['pdf_qqbar'] = { + 'name': 'pdf_qqbar', + 'type': 'lnN', + 'samples': { + 'Zg': '1.04', + 'ZgS': '1.04', + 'WZ': '1.04', + 'ZZ': '1.04', + }, + 'cuts' : fitcuts + } + +nuisances['pdf_Higgs_gg_ACCEPT'] = { + 'name' : 'pdf_Higgs_gg_ACCEPT', + 'samples' : { + 'ggZH_hww': '1.006', + }, + 'type' : 'lnN', + 'cuts' : fitcuts + } + +nuisances['pdf_Higgs_qqbar_ACCEPT'] = { + 'name' : 'pdf_Higgs_qqbar_ACCEPT', + 'type' : 'lnN', + 'samples' : { + 'WH_hww' : '1.003', + 'ZH_hww' : '1.002', + 'WH_htt' : '1.003', + 'ZH_htt' : '1.002', + }, + 'cuts' : fitcuts + } + +nuisances['pdf_qqbar_ACCEPT'] = { + 'name' : 'pdf_qqbar_ACCEPT', + 'type' : 'lnN', + 'samples' : { + 'WZ' : '1.001', + 'ZZ' : '1.001', + }, + 'cuts' : fitcuts + } + +nuisances['pdf_gg_ACCEPT'] = { + 'name': 'pdf_gg_ACCEPT', + 'samples': { + 'ggWW': '1.006', + }, + 'type': 'lnN', + 'cuts' : fitcuts +} + +# nuisances['PS_ISR'] = { +# 'name': 'PS_ISR', +# 'kind': 'weight', +# 'type': 'shape', +# 'samples': { +# 'ggZH_hww': ['1.066107*(nCleanGenJet==0) + 1.047857*(nCleanGenJet==1) + 1.030005*(nCleanGenJet==2) + 1.005028*(nCleanGenJet>=3)', '0.921874*(nCleanGenJet==0) + 0.941939*(nCleanGenJet==1) + 0.962282*(nCleanGenJet==2) + 0.991580*(nCleanGenJet>=3)'], +# 'ZH_hww': ['1.000684*(nCleanGenJet==0) + 1.000924*(nCleanGenJet==1) + 1.001683*(nCleanGenJet==2) + 1.002104*(nCleanGenJet>=3)', '0.999150*(nCleanGenJet==0) + 0.998821*(nCleanGenJet==1) + 0.997859*(nCleanGenJet==2) + 0.997316*(nCleanGenJet>=3)'], +# 'WZ': ['1.002552*(nCleanGenJet==0) + 1.010286*(nCleanGenJet==1) + 1.014420*(nCleanGenJet==2) + 1.006226*(nCleanGenJet>=3)', '0.996802*(nCleanGenJet==0) + 0.987227*(nCleanGenJet==1) + 0.982005*(nCleanGenJet==2) + 0.992030*(nCleanGenJet>=3)'], +# 'ZZ': ['1.003210*(nCleanGenJet==0) + 1.005480*(nCleanGenJet==1) + 1.004674*(nCleanGenJet==2) + 0.987845*(nCleanGenJet>=3)', '0.995997*(nCleanGenJet==0) + 0.993056*(nCleanGenJet==1) + 0.993659*(nCleanGenJet==2) + 1.014695*(nCleanGenJet>=3)'], +# }, +# 'cuts' : fitcuts +# } + +# nuisances['PS_FSR'] = { +# 'name': 'PS_FSR', +# 'kind': 'weight', +# 'type': 'shape', +# 'samples': { +# 'ggZH_hww': ['0.987316*(nCleanGenJet==0) + 0.986764*(nCleanGenJet==1) + 0.996498*(nCleanGenJet==2) + 1.004161*(nCleanGenJet>=3)', '1.019871*(nCleanGenJet==0) + 1.013853*(nCleanGenJet==1) + 1.005229*(nCleanGenJet==2) + 0.998573*(nCleanGenJet>=3)'], +# 'ZH_hww': ['0.992867*(nCleanGenJet==0) + 0.992845*(nCleanGenJet==1) + 0.999470*(nCleanGenJet==2) + 1.007245*(nCleanGenJet>=3)', '1.012465*(nCleanGenJet==0) + 1.012743*(nCleanGenJet==1) + 1.003215*(nCleanGenJet==2) + 0.991286*(nCleanGenJet>=3)'], +# 'WZ': ['0.992987*(nCleanGenJet==0) + 0.993725*(nCleanGenJet==1) + 1.000617*(nCleanGenJet==2) + 1.010869*(nCleanGenJet>=3)', '1.011267*(nCleanGenJet==0) + 1.010097*(nCleanGenJet==1) + 0.999445*(nCleanGenJet==2) + 0.983609*(nCleanGenJet>=3)'], +# 'ZZ': ['0.997245*(nCleanGenJet==0) + 0.998689*(nCleanGenJet==1) + 1.004475*(nCleanGenJet==2) + 1.011440*(nCleanGenJet>=3)', '1.004482*(nCleanGenJet==0) + 1.002081*(nCleanGenJet==1) + 0.992617*(nCleanGenJet==2) + 0.981314*(nCleanGenJet>=3)'], +# }, +# 'cuts' : fitcuts +# } + +nuisances['PU'] = { + 'name': 'CMS_PU_2018', + 'kind': 'weight', + 'type': 'shape', + 'samples': { + 'WZ': ['1.00103945*(puWeightUp/puWeight)', '0.99898154*(puWeightDown/puWeight)'], + 'ZZ': ['1.00125684*(puWeightUp/puWeight)', '0.99878368*(puWeightDown/puWeight)'], + 'ZH_hww': ['1.00129463*(puWeightUp/puWeight)', '0.99847264*(puWeightDown/puWeight)'], + 'ggZH_hww': ['0.99973837*(puWeightUp/puWeight)', '1.0001526*(puWeightDown/puWeight)'], + }, + 'AsLnN': '1', + 'cuts' : fitcuts +} + +### PU ID SF uncertainty +puid_syst = ['Jet_PUIDSF_up/Jet_PUIDSF', 'Jet_PUIDSF_down/Jet_PUIDSF'] + +nuisances['jetPUID'] = { + 'name': 'CMS_PUID_2018', + 'kind': 'weight', + 'type': 'shape', + 'samples': dict((skey, puid_syst) for skey in mc) +} + +nuisances['UE_whss'] = { + 'name' : 'UE_whss', + 'skipCMS' : 1, + 'type' : 'lnN', + 'samples' : { + 'WH_hww' : '1.015', + 'ZH_hww' : '1.015', + 'ggZH_hww' : '1.015', + 'WH_htt' : '1.015', + 'ZH_htt' : '1.015', + }, + 'cuts' : fitcuts +} + +nuisances['WZ3l2jnorm'] = { + 'name' : 'CMS_hww_WZ3l2jnorm', + 'samples' : { + 'WZ' : '1.00', + }, + 'type' : 'rateParam', + 'cuts' : [ + 'zh3l_WZ_CR_2j', + 'zh3l_WZ_CR_2j_met', + 'zh3l_SR_2j', + 'zh3l_SR_2j_met', + ] + } + +nuisances['WZ3l1jnorm'] = { + 'name' : 'CMS_hww_WZ3l1jnorm', + 'samples' : { + 'WZ' : '1.00', + }, + 'type' : 'rateParam', + 'cuts' : [ + 'zh3l_WZ_CR_1j', + 'zh3l_WZ_CR_1j_met', + 'zh3l_SR_1j', + 'zh3l_SR_1j_met', + ] + } + +#### Top + +apply_on = { + 'top': [ + '(topGenPt * antitopGenPt <= 0.) * 1.0816 + (topGenPt * antitopGenPt > 0.)', + '(topGenPt * antitopGenPt <= 0.) * 0.9184 + (topGenPt * antitopGenPt > 0.)' + ] +} + +nuisances['singleTopToTTbar'] = { + 'name': 'singleTopToTTbar', + 'skipCMS': 1, + 'kind': 'weight', + 'type': 'shape', + 'samples': apply_on, + 'cuts' : fitcuts +} + +nuisances['TopPtRew'] = { + 'name': 'CMS_topPtRew', # Theory uncertainty + 'kind': 'weight', + 'type': 'shape', + 'samples': {'top': ["1.", "1./Top_pTrw"]}, + 'symmetrize': True, + 'cuts' : fitcuts +} + +#### FAKES + +fakeW_EleUp = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lElUp / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_EleDown = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lElDown / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_MuUp = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lMuUp / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_MuDown = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lMuDown / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_statEleUp = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lstatElUp / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_statEleDown = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lstatElDown / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_statMuUp = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lstatMuUp / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_statMuDown = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lstatMuDown / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' + +nuisances['fake_syst_e'] = { + 'name' : 'CMS_fake_syst_e', + 'type' : 'lnN', + 'samples' : { + 'Fake_e' : '1.30', + }, + 'cuts' : fitcuts +} + +nuisances['fake_syst_m'] = { + 'name' : 'CMS_fake_syst_m', + 'type' : 'lnN', + 'samples' : { + 'Fake_m' : '1.30', + }, + 'cuts' : fitcuts +} + +nuisances['fake_ele'] = { + 'name' : 'CMS_fake_e_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : { + 'Fake' : [ fakeW_EleUp , fakeW_EleDown ], + }, + 'cuts' : fitcuts +} + +nuisances['fake_ele_stat'] = { + 'name' : 'CMS_fake_stat_e_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : { + 'Fake' : [ fakeW_statEleUp , fakeW_statEleDown ], + }, + 'cuts' : fitcuts +} + +nuisances['fake_mu'] = { + 'name' : 'CMS_fake_m_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : { + 'Fake' : [ fakeW_MuUp , fakeW_MuDown ], + }, + 'cuts' : fitcuts +} + +nuisances['fake_mu_stat'] = { + 'name' : 'CMS_fake_stat_m_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : { + 'Fake' : [ fakeW_statMuUp , fakeW_statMuDown ], + }, + 'cuts' : fitcuts +} + +###### B-tagger + +for shift in ['jes', 'lf', 'hf', 'hfstats1', 'hfstats2', 'lfstats1', 'lfstats2', 'cferr1', 'cferr2']: + btag_syst = ['(btagSF%sup)/(btagSF)' % shift, '(btagSF%sdown)/(btagSF)' % shift] + + name = 'CMS_btag_%s' % shift + if 'stats' in shift: + name += '_2018' + + nuisances['btag_shape_%s' % shift] = { + 'name': name, + 'kind': 'weight', + 'type': 'shape', + 'samples': dict((skey, btag_syst) for skey in mc), + 'cuts' : fitcuts + } + +#### Trigger Efficiency + +trig_syst = ['((TriggerEffWeight_3l_u)/(TriggerEffWeight_3l))*(TriggerEffWeight_3l>0.02) + (TriggerEffWeight_3l<=0.02)', '(TriggerEffWeight_3l_d)/(TriggerEffWeight_3l)'] + +nuisances['trigg'] = { + 'name' : 'CMS_eff_hwwtrigger_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : dict((skey, trig_syst) for skey in mc), + 'cuts' : fitcuts +} + +##### Electron Efficiency and energy scale + +id_syst_ele = [ 'SFweightEleUp', 'SFweightEleDown'] + +nuisances['eff_e'] = { + 'name' : 'CMS_eff_e_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : dict((skey, id_syst_ele) for skey in mc), + 'cuts' : fitcuts +} + +nuisances['electronpt'] = { + 'name' : 'CMS_scale_e_2018', + 'kind' : 'suffix', + 'type' : 'shape', + 'mapUp' : 'ElepTup', + 'mapDown' : 'ElepTdo', + 'samples' : dict((skey, ['1', '1']) for skey in mc), + 'folderUp' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__ElepTup_suffix', + 'folderDown' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__ElepTdo_suffix', + 'AsLnN' : '1', + 'cuts' : fitcuts +} + +###### Muon Efficiency and energy scale + +id_syst_mu = [ 'SFweightMuUp', 'SFweightMuDown'] + +nuisances['eff_m'] = { + 'name' : 'CMS_eff_m_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : dict((skey, id_syst_mu) for skey in mc), + 'cuts' : fitcuts +} + +nuisances['muonpt'] = { + 'name' : 'CMS_scale_m_2018', + 'kind' : 'suffix', + 'type' : 'shape', + 'mapUp' : 'MupTup', + 'mapDown' : 'MupTdo', + 'samples' : dict((skey, ['1', '1']) for skey in mc), + 'folderUp' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__MupTup_suffix', + 'folderDown' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__MupTdo_suffix', + 'AsLnN' : '1', + 'cuts' : fitcuts +} + +####### Jet energy scale + +jes_systs = ['JESAbsolute','JESAbsolute_2018','JESBBEC1','JESBBEC1_2018','JESEC2','JESEC2_2018','JESFlavorQCD','JESHF','JESHF_2018','JESRelativeBal','JESRelativeSample_2018'] +folderup = "" +folderdo = "" + +for js in jes_systs: + if 'Absolute' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESAbsoluteup_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESAbsolutedo_suffix' + elif 'BBEC1' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESBBEC1up_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESBBEC1do_suffix' + elif 'EC2' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESEC2up_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESEC2do_suffix' + elif 'HF' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESHFup_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESHFdo_suffix' + elif 'Relative' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESRelativeup_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESRelativedo_suffix' + elif 'FlavorQCD' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESFlavorQCDup_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESFlavorQCDdo_suffix' + + nuisances[js] = { + 'name': 'CMS_scale_'+js, + 'kind': 'suffix', + 'type': 'shape', + 'mapUp': js+'up', + 'mapDown': js+'do', + 'samples': dict((skey, ['1', '1']) for skey in mc), + 'folderUp' : folderup, + 'folderDown' : folderdo, + 'AsLnN': '1', + 'cuts' : fitcuts + } + +###### Jet energy resolution +nuisances['JER'] = { + 'name' : 'CMS_res_j_2018', + 'kind': 'suffix', + 'type': 'shape', + 'mapUp': 'JERup', + 'mapDown': 'JERdo', + 'samples': dict((skey, ['1', '1']) for skey in mc), + 'folderUp': treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JERup_suffix', + 'folderDown': treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JERdo_suffix', + 'AsLnN' : '1' +} +##### MET energy scale + +nuisances['met'] = { + 'name' : 'CMS_scale_met_2018', + 'kind' : 'suffix', + 'type' : 'shape', + 'mapUp' : 'METup', + 'mapDown' : 'METdo', + 'samples' : dict((skey, ['1', '1']) for skey in mc), + 'folderUp' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__METup_suffix', + 'folderDown' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__METdo_suffix', + 'AsLnN' : '1', + 'cuts' : fitcuts +} + +# Use the following if you want to apply the automatic combine MC stat nuisances. +nuisances['stat'] = { + 'type' : 'auto', + 'maxPoiss' : '10', + 'includeSignal' : '1', + # nuisance ['maxPoiss'] = Number of threshold events for Poisson modelling + # nuisance ['includeSignal'] = Include MC stat nuisances on signal processes (1=True, 0=False) + 'samples' : {}, + 'cuts' : fitcuts + } + +for n in nuisances.values(): + n['skipCMS'] = 1 diff --git a/ZH3l_BDT/zh3l_run2_rdf/plot.py b/ZH3l_BDT/zh3l_run2_rdf/plot.py new file mode 100644 index 00000000..e53e2f8a --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/plot.py @@ -0,0 +1,249 @@ +# plot configuration +groupPlot = {} + +# Groups of samples to improve the plots. +# If not defined, normal plots is used + +groupPlot['DY'] = { + 'nameHR' : 'DY', + 'color': 616, # kMagenta + 'isSignal' : 0, + 'samples' : ['DY'] +} + +groupPlot['WW'] = { + 'nameHR' : 'WW', + 'color': 632, # kRed + 'isSignal' : 0, + 'samples' : ['WW'] +} + +groupPlot['Zg'] = { + 'nameHR' : 'Zg', + 'color': 859, # kAzure -1 + 'isSignal' : 0, + 'samples' : ['Zg'] + } + +groupPlot['ZgS'] = { + 'nameHR' : 'ZgS', + 'color': 432, # kCyan + 'isSignal' : 0, + 'samples' : ['ZgS'] + } + +groupPlot['WZ'] = { + 'nameHR' : "WZ", + 'isSignal' : 0, + 'color' : 400, # Yellow + 'samples' : ['WZ'] + } + +groupPlot['ZZ'] = { + 'nameHR' : "ZZ", + 'isSignal' : 0, + 'color' : 617, # kViolet + 1 + 'samples' : ['ZZ'] + } + +groupPlot['VVV'] = { + 'nameHR' : 'VVV', + 'isSignal' : 0, + 'color': 806, # kOrange + 6 + 'samples' : ['VVV'] + } + + +groupPlot['top'] = { + 'nameHR' : 'top', + 'color': 416, # kGreen + 'isSignal' : 0, + 'samples' : ['top'] +} + +groupPlot['ttV'] = { + 'nameHR' : 'ttV', + 'color': 419, # kGreen+3 + 'isSignal' : 0, + 'samples' : ['ttV'] +} + +groupPlot['Fake'] = { + 'nameHR' : 'Non-prompt', + 'isSignal' : 0, + 'color': 921, # kGray + 1 + 'samples' : ['Fake_e', 'Fake_m'] + } + +groupPlot['Higgs'] = { + 'nameHR' : 'Higgs', + 'isSignal' : 1, + 'color': 632, # kRed + 'scaleMultiplicativeOverlaid' : 10.0, + 'samples' : ['WH_hww', 'ZH_hww', 'ggZH_hww', 'ZH_htt', 'ttH_hww', 'WH_htt'] + } + +# Individual plots +plot = {} + +plot['DY'] = { + 'nameHR' : 'DY', + 'color': 858, # kAzure -2 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['WW'] = { + 'nameHR' : 'WW', + 'color': 858, # kAzure -2 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +#plot['WWewk'] = { +# 'nameHR' : 'WWewk', +# 'color': 858, # kAzure -2 +# 'isSignal' : 0, +# 'isData' : 0, +# 'scale' : 1.0 +# } + +#plot['ggWW'] = { +# 'nameHR' : 'ggWW', +# 'color': 858, # kAzure -2 +# 'isSignal' : 0, +# 'isData' : 0, +# 'scale' : 1.0 +# } + +plot['Zg'] = { + 'nameHR' : 'Zg', + 'color': 859, # kAzure -1 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['ZgS'] = { + 'nameHR' : 'ZgS', + 'color': 859, # kAzure -1 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['WZ'] = { + 'nameHR' : 'WZ', + 'color': 858, # kAzure -2 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + #'scale' : 0.89 #1j norm + #'scale' : 1.22 #2j norm + } + +plot['ZZ'] = { + 'nameHR' : 'ZZ', + 'color': 856, # kAzure -4 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['VVV'] = { + 'color': 857, # kAzure -3 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['top'] = { + 'color': 419, # kGreen+3 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 # ele/mu trigger efficiency datadriven +} + +plot['ttV'] = { + 'color': 419, # kGreen+3 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 # ele/mu trigger efficiency datadriven +} + +plot['Fake_e'] = { + 'color': 921, # kGray + 1 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['Fake_m'] = { + 'color': 921, # kGray + 1 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['ttH_hww'] = { + 'nameHR' : 'ttH', + 'color': 632+3, # kRed+3 + 'isSignal' : 1, + 'isData' : 0, + 'scale' : 1 + } + +plot['ZH_hww'] = { + 'nameHR' : 'ZH', + 'color': 632+3, # kRed+3 + 'isSignal' : 1, + 'isData' : 0, + 'scale' : 1 + } + +plot['ggZH_hww'] = { + 'nameHR' : 'ggZH', + 'color': 632+4, # kRed+4 + 'isSignal' : 1, + 'isData' : 0, + 'scale' : 1 + } + +plot['WH_hww'] = { + 'nameHR' : 'WH', + 'color': 632+2, # kRed+2 + 'isSignal' : 1, + 'isData' : 0, + 'scale' : 1 + } + +# plot['WH_htt'] = { +# 'nameHR' : 'WH htt', +# 'color': 632+1, # kRed+4 +# 'isSignal' : 1, +# 'isData' : 0, +# 'scale' : 1 +# } + +plot['ZH_htt'] = { + 'nameHR' : 'ZH htt', + 'color': 632+1, # kRed+4 + 'isSignal' : 1, + 'isData' : 0, + 'scale' : 1 + } + +plot['DATA'] = { + 'nameHR' : 'Data', + 'color': 1 , + 'isSignal' : 0, + 'isData' : 1 , + 'isBlind' : 0 + } + +# additional options +legend = {} +legend['lumi'] = 'L = 59.7/fb' +legend['sqrt'] = '#sqrt{s} = 13 TeV' diff --git a/ZH3l_BDT/zh3l_run2_rdf/samples.py b/ZH3l_BDT/zh3l_run2_rdf/samples.py new file mode 100644 index 00000000..3425ab65 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/samples.py @@ -0,0 +1,416 @@ +import os +import subprocess + +# global getSampleFiles +# from LatinoAnalysis.Tools.commonTools import getSampleFiles, addSampleWeight, getBaseWnAOD +# It looks like getBaseWnAOD and addSampleWeight from Latinos does the same job as CombineBaseW function from makeShapesRDF. Similarly getSampleFiles function from Latinos is the same as nanoGetSampleFiles from makeShapesRDF. + +from mkShapesRDF.lib.search_files import SearchFiles +searchFiles = SearchFiles() +redirector = "" +limitFiles = -1 +samples = {} + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + return [(name, _files[:limitFiles])] + else: + return [(name, _files)] + +def getSampleFilesNano(inputDir,Sample,absPath=False): + # return getSampleFiles(inputDir,Sample,absPath,'nanoLatino_') + return nanoGetSampleFiles(inputDir, Sample) # getSampleFiles replaced with nanoGetSampleFiles(path, name) (DS, 03Nov25) + +def CombineBaseW(samples, proc, samplelist): + _filtFiles = list(filter(lambda k: k[0] in samplelist, samples[proc]["name"])) + _files = list(map(lambda k: k[1], _filtFiles)) + _l = list(map(lambda k: len(k), _files)) + leastFiles = _files[_l.index(min(_l))] + dfSmall = ROOT.RDataFrame("Runs", leastFiles) + s = dfSmall.Sum("genEventSumw").GetValue() + f = ROOT.TFile(leastFiles[0]) + t = f.Get("Events") + t.GetEntry(1) + xs = t.baseW * s + + __files = [] + for f in _files: + __files += f + df = ROOT.RDataFrame("Runs", __files) + s = df.Sum("genEventSumw").GetValue() + newbaseW = str(xs / s) + return newbaseW # "/baseW is used after getBaseWnAOD/CombineBaseW calls in this code" + # weight = newbaseW + "/baseW" + + # for iSample in samplelist: + # addSampleWeight(samples, proc, iSample, weight) + + +def addSampleWeight(samples, sampleName, sampleNameType, weight): + obj = list(filter(lambda k: k[0] == sampleNameType, samples[sampleName]["name"]))[0] + samples[sampleName]["name"] = list( + filter(lambda k: k[0] != sampleNameType, samples[sampleName]["name"]) + ) + if len(obj) > 2: + samples[sampleName]["name"].append( + (obj[0], obj[1], obj[2] + "*(" + weight + ")") + ) + else: + samples[sampleName]["name"].append((obj[0], obj[1], "(" + weight + ")")) + + +############################################## +###### Tree Directory according to site ###### +############################################## + +SITE=os.uname()[1] +xrootdPath='' +if 'iihe' in SITE : + xrootdPath = 'dcap://maite.iihe.ac.be/' + treeBaseDir = '/pnfs/iihe/cms/store/user/xjanssen/HWW2015/' +elif 'cern' in SITE : + #xrootdPath='root://eoscms.cern.ch/' + treeBaseDir = '/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/' + +directory = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7' + +################################################ +############ NUMBER OF LEPTONS ################# +################################################ + +#Nlep='2' +Nlep='3' +#Nlep='4' + +################################################ +############### Lepton WP ###################### +################################################ + +eleWP='mvaFall17V1Iso_WP90' +#eleWP='mvaFall17V1Iso_WP90_SS' +#eleWP='mvaFall17V2Iso_WP90' +#eleWP='mvaFall17V2Iso_WP90_SS' +muWP ='cut_Tight_HWWW' +eleWP_new = 'mvaFall17V1Iso_WP90_tthmva_70' +muWP_new = 'cut_Tight_HWWW_tthmva_80' + +LepWPCut = 'LepCut'+Nlep+'l__ele_'+eleWP_new+'__mu_'+muWP_new +#LepWPCut = 'LepCut'+Nlep+'l__ele_'+eleWP+'__mu_'+muWP +#LepWPweight = 'ttHMVA_SF_3l[0]' #SF for new WPs, defined in aliases +LepWPweight = 'LepSF'+Nlep+'l__ele_'+eleWP_new+'__mu_'+muWP_new + +################################################ +############ BASIC MC WEIGHTS ################## +################################################ + +XSWeight = 'XSWeight' +SFweight = 'SFweight'+Nlep+'l*'+LepWPweight+'*'+LepWPCut+'*Jet_PUIDSF' +PromptGenLepMatch = 'PromptGenLepMatch'+Nlep+'l' + +################################################ +############## FAKE WEIGHTS #################### +################################################ + +#eleWP_new = 'mvaFall17V1Iso_WP90_tthmva_70' +#muWP_new = 'cut_Tight_HWWW_tthmva_80' + +if Nlep == '2' : + fakeW = 'fakeW2l_ele_'+eleWP_new+'_mu_'+muWP_new + #fakeW = 'fakeW2l_ele_'+eleWP+'_mu_'+muWP +else: + fakeW = 'fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_'+Nlep+'l' + #fakeW = 'fakeW_ele_'+eleWP+'_mu_'+muWP+'_'+Nlep+'l' + +################################################ +############### B-Tag WP ###################### +################################################ + +SFweight += '*btagSF' #define in aliases.py + +################################################ +############ MET FILTERS ################### +################################################ + +METFilter_MC = 'METFilter_MC' +METFilter_DATA = 'METFilter_DATA' + +################################################ +############ DATA DECLARATION ################## +################################################ + +DataRun = [ + ['A','Run2018A-02Apr2020-v1'] , + ['B','Run2018B-02Apr2020-v1'] , + ['C','Run2018C-02Apr2020-v1'] , + ['D','Run2018D-02Apr2020-v1'] , + ] + +DataSets = ['MuonEG','DoubleMuon','SingleMuon','EGamma'] + +DataTrig = { + 'MuonEG' : 'Trigger_ElMu' , + 'DoubleMuon' : '!Trigger_ElMu && Trigger_dblMu' , + 'SingleMuon' : '!Trigger_ElMu && !Trigger_dblMu && Trigger_sngMu' , + 'EGamma' : '!Trigger_ElMu && !Trigger_dblMu && !Trigger_sngMu && (Trigger_sngEl || Trigger_dblEl)' , + } + +########################################### +############# BACKGROUNDS ############### +########################################### + +############ DY ############ + +ptllDYW_NLO = '(0.87*(gen_ptll<10)+(0.379119+0.099744*gen_ptll-0.00487351*gen_ptll**2+9.19509e-05*gen_ptll**3-6.0212e-07*gen_ptll**4)*(gen_ptll>=10 && gen_ptll<45)+(9.12137e-01+1.11957e-04*gen_ptll-3.15325e-06*gen_ptll**2-4.29708e-09*gen_ptll**3+3.35791e-11*gen_ptll**4)*(gen_ptll>=45 && gen_ptll<200) + 1*(gen_ptll>200))' +ptllDYW_LO = '((0.632927+0.0456956*gen_ptll-0.00154485*gen_ptll*gen_ptll+2.64397e-05*gen_ptll*gen_ptll*gen_ptll-2.19374e-07*gen_ptll*gen_ptll*gen_ptll*gen_ptll+6.99751e-10*gen_ptll*gen_ptll*gen_ptll*gen_ptll*gen_ptll)*(gen_ptll>0)*(gen_ptll<100)+(1.41713-0.00165342*gen_ptll)*(gen_ptll>=100)*(gen_ptll<300)+1*(gen_ptll>=300))' +Zgfilter = '( !(Sum(PhotonGen_isPrompt==1 && PhotonGen_pt>15 && abs(PhotonGen_eta)<2.6) > 0 && Sum(LeptonGen_isPrompt==1 && LeptonGen_pt>15)>=2) )' #Zg sample uses photon pt > 15, lepton pt > 15 + +samples['DY'] = { 'name' : getSampleFilesNano(directory,'DYJetsToLL_M-10to50-LO') + + getSampleFilesNano(directory,'DYJetsToLL_M-10to50-LO_ext1') + + getSampleFilesNano(directory,'DYJetsToLL_M-50-LO') + + getSampleFilesNano(directory, 'DYJetsToLL_M-50') + + getSampleFilesNano(directory, 'DYJetsToLL_M-50_ext2') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-100to200') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-200to400') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-400to600') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-600toInf') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-70to100') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-100to200') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-200to400') + # + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-400to600') + # + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-400to600_ext2') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-600to800') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-800to1200') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-1200to2500') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-2500toInf'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC +'*'+Zgfilter, + 'FilesPerJob' : 5, + 'suppressNegative' :['all'], + 'suppressNegativeNuisances' :['all'], + } + +M10baseW = '1'#CombineBaseW(directory,'Autumn18_102X_nAODv7_Full2018v7',['DYJetsToLL_M-10to50-LO','DYJetsToLL_M-10to50-LO_ext1']) +#HT400baseW = getBaseWnAOD(directory,'Autumn18_102X_nAODv7_Full2018v7',['DYJetsToLL_M-50_HT-400to600']) + +addSampleWeight(samples,'DY','DYJetsToLL_M-10to50-LO' ,ptllDYW_LO+'*(LHE_HT<100.0)*'+M10baseW+'/baseW') +addSampleWeight(samples,'DY','DYJetsToLL_M-10to50-LO_ext1' ,ptllDYW_LO+'*(LHE_HT<100.0)*'+M10baseW+'/baseW') +addSampleWeight(samples,'DY','DYJetsToLL_M-50-LO' ,ptllDYW_LO+'*(LHE_HT<70.0)') +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-100to200' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-200to400' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-400to600' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-600toInf' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-70to100' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-100to200' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-200to400' ,ptllDYW_LO) +#addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-400to600' ,ptllDYW_LO+'*'+HT400baseW+'/baseW') +#addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-400to600_ext2',ptllDYW_LO+'*'+HT400baseW+'/baseW') +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-600to800' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-800to1200' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-1200to2500' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-2500toInf' ,ptllDYW_LO) +addSampleWeight(samples,'DY', 'DYJetsToLL_M-50' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_ext2' ,ptllDYW_LO) + +############ Top ############ + +samples['top'] = { 'name' : getSampleFilesNano(directory,'TTTo2L2Nu') + + getSampleFilesNano(directory,'ST_s-channel_ext1') + # + getSampleFilesNano(directory,'ST_t-channel_antitop') + + getSampleFilesNano(directory,'ST_t-channel_top') + + getSampleFilesNano(directory,'ST_tW_antitop_ext1') + + getSampleFilesNano(directory,'ST_tW_top_ext1'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + 'FilesPerJob' : 6, + } + +addSampleWeight(samples,'top','TTTo2L2Nu','Top_pTrw') + +samples['ttV'] = { 'name' : getSampleFilesNano(directory,'TTWJetsToLNu') + + getSampleFilesNano(directory,'TTZjets'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + 'FilesPerJob' : 5, + } + + +############ WW ############ + + +samples['WW'] = { 'name' : getSampleFilesNano(directory,'WWTo2L2Nu'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*nllW', + } + +#samples['WWewk'] = { 'name' : getSampleFilesNano(directory,'WpWmJJ_EWK_QCD_noTop_noHiggs') +# + getSampleFilesNano(directory,'WpWpJJ_EWK_QCD'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*(Sum$(abs(GenPart_pdgId)==6 || GenPart_pdgId==25)==0)*(lhe_mW1[0] > 60. && lhe_mW1[0] < 100. && lhe_mW2[0] > 60. && lhe_mW2[0] < 100.)', +# } + +#samples['ggWW'] = { 'name' : getSampleFilesNano(directory,'GluGluToWWToENEN') +# + getSampleFilesNano(directory,'GluGluToWWToENMN') +# + getSampleFilesNano(directory,'GluGluToWWToENTN') +# + getSampleFilesNano(directory,'GluGluToWWToMNEN') +# + getSampleFilesNano(directory,'GluGluToWWToMNMN') +# + getSampleFilesNano(directory,'GluGluToWWToMNTN') +# + getSampleFilesNano(directory,'GluGluToWWToTNEN') +# + getSampleFilesNano(directory,'GluGluToWWToTNMN') +# + getSampleFilesNano(directory,'GluGluToWWToTNTN'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*1.53/1.4', +# } + +###### Zg + +samples['Zg'] = { 'name' : getSampleFilesNano(directory,'ZGToLLG'), + 'weight' : XSWeight+'*'+SFweight+'*'+METFilter_MC + '*(Gen_ZGstar_mass <= 0)', + 'FilesPerJob' : 6 , + } + +###### Zg* + +samples['ZgS'] = { 'name' : getSampleFilesNano(directory,'ZGToLLG'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*(Gen_ZGstar_mass > 0)', + 'FilesPerJob' : 4 , + } + +##### WZ + +samples['WZ'] = { 'name': getSampleFilesNano(directory,'WZTo3LNu_mllmin01') + + getSampleFilesNano(directory,'WZTo2L2Q'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*(gstarHigh)' , + 'FilesPerJob' : 5 , + } + +##### ZZ + +samples['ZZ'] = { 'name' : getSampleFilesNano(directory,'ZZTo2L2Nu_ext1') + + getSampleFilesNano(directory,'ZZTo2L2Nu_ext2') + + getSampleFilesNano(directory,'ZZTo2L2Q') + + getSampleFilesNano(directory,'ZZTo4L_ext1') + + getSampleFilesNano(directory,'ZZTo4L_ext2') + #+ getSampleFilesNano(directory,'ggZZ4m') #Missing file for ElepTup + + getSampleFilesNano(directory,'ggZZ4m_ext1') + + getSampleFilesNano(directory,'ggZZ4t') + + getSampleFilesNano(directory,'ggZZ2e2t') + + getSampleFilesNano(directory,'ggZZ2m2t') + + getSampleFilesNano(directory,'ggZZ2e2m'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC, + 'FilesPerJob' : 3, + } + +ZZ2LbaseW = '1'#CombineBaseW(directory,'Autumn18_102X_nAODv7_Full2018v7',['ZZTo2L2Nu_ext1','ZZTo2L2Nu_ext2']) +ZZ4LbaseW = '1'#CombineBaseW(directory,'Autumn18_102X_nAODv7_Full2018v7',['ZZTo4L_ext1', 'ZZTo4L_ext2']) +#ggZZbaseW = getBaseWnAOD(directory,'Autumn18_102X_nAODv7_Full2018v7',['ggZZ4m', 'ggZZ4m_ext1']) + +addSampleWeight(samples,'ZZ','ZZTo2L2Nu_ext1',"1.07*"+ZZ2LbaseW+"/baseW") ## The non-ggZZ NNLO/NLO k-factor, cited from https://arxiv.org/abs/1405.2219v1 +addSampleWeight(samples,'ZZ','ZZTo2L2Nu_ext2',"1.07*"+ZZ2LbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ZZTo2L2Q', "1.07") +addSampleWeight(samples,'ZZ','ZZTo4L_ext1', "1.07*"+ZZ4LbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ZZTo4L_ext2', "1.07*"+ZZ4LbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ggZZ2e2t', "1.68") ## The NLO/LO k-factor, cited from https://arxiv.org/abs/1509.06734v1 +addSampleWeight(samples,'ZZ','ggZZ2m2t', "1.68") +addSampleWeight(samples,'ZZ','ggZZ2e2m', "1.68") +#addSampleWeight(samples,'ZZ','ggZZ4m', "1.68*"+ggZZbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ggZZ4m_ext1', "1.68") +addSampleWeight(samples,'ZZ','ggZZ4t', "1.68") + +############ VVV ############ + +samples['VVV'] = { 'name' : getSampleFilesNano(directory,'ZZZ') + + getSampleFilesNano(directory,'WZZ') + + getSampleFilesNano(directory,'WWZ') + + getSampleFilesNano(directory,'WWW'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +########################################## +################ SIGNALS ################# +########################################## + +############ ZH H->WW ############ + +samples['ZH_hww'] = { 'name' : getSampleFilesNano(directory,'HZJ_HToWW_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +samples['ggZH_hww'] = { 'name' : getSampleFilesNano(directory,'GluGluZH_HToWW_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +############ WH H->WW ############ + +samples['WH_hww'] = { 'name' : getSampleFilesNano(directory,'HWplusJ_HToWW_M125') + + getSampleFilesNano(directory,'HWminusJ_HToWW_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +############ ttH ############ + +samples['ttH_hww'] = { 'name' : getSampleFilesNano(directory,'ttHToNonbb_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +############ bbH ############ +# Not available for Latinos 2016 v6 + +############ H->TauTau ############ + +#samples['ggH_htt'] = { 'name' : getSampleFilesNano(directory,'GluGluHToTauTau_M125'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , +# } + +#samples['qqH_htt'] = { 'name' : getSampleFilesNano(directory,'VBFHToTauTau_M125'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , +# } + +samples['ZH_htt'] = { 'name' : getSampleFilesNano(directory,'HZJ_HToTauTau_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +samples['WH_htt'] = { 'name' : getSampleFilesNano(directory,'HWplusJ_HToTauTau_M125') + + getSampleFilesNano(directory,'HWminusJ_HToTauTau_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +########################################### +################## FAKE ################### +########################################### + +samples['Fake'] = { 'name': [ ] , + 'weight' : fakeW+'*'+METFilter_DATA, + 'weights' : [ ] , + 'isData': ['all'], + 'FilesPerJob' : 500 , + 'suppressNegativeNuisances' :['all'], + } + +directory = treeBaseDir+'Run2018_102X_nAODv7_Full2018v7/DATAl1loose2018v7__l2loose__fakeW/' +#directory = treeBaseDir+'Run2018_102X_nAODv6_Full2018v6/DATAl1loose2018v6__l2loose__fakeW/' +for Run in DataRun : + for DataSet in DataSets : + FileTarget = getSampleFilesNano(directory,DataSet+'_'+Run[1],True) + for iFile in FileTarget: + samples['Fake']['name'].append(iFile) + samples['Fake']['weights'].append(DataTrig[DataSet]) + +samples['Fake']['subsamples'] = { + 'e': 'abs(ZH3l_pdgid_l) == 11', + 'm': 'abs(ZH3l_pdgid_l) == 13' +} + +########################################### +################## DATA ################### +########################################### + +samples['DATA'] = { 'name': [ ] , + 'weight' : METFilter_DATA+'*'+LepWPCut, + 'weights' : [ ], + 'isData': ['all'], + 'FilesPerJob' : 500 , + } + +directory = treeBaseDir+'/Run2018_102X_nAODv7_Full2018v7/DATAl1loose2018v7__l2loose__l2tightOR2018v7/' +for Run in DataRun : + for DataSet in DataSets : + FileTarget = getSampleFilesNano(directory,DataSet+'_'+Run[1],True) + for iFile in FileTarget: + samples['DATA']['name'].append(iFile) + samples['DATA']['weights'].append(DataTrig[DataSet]) + diff --git a/ZH3l_BDT/zh3l_run2_rdf/structure.py b/ZH3l_BDT/zh3l_run2_rdf/structure.py new file mode 100644 index 00000000..44147676 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/structure.py @@ -0,0 +1,113 @@ +# structure configuration for datacard +# keys here must match keys in samples.py +structure = {} + +# Backgrounds +structure['Fake_e'] = { + 'isSignal' : 0, + 'isData' : 0, + } + +structure['Fake_m'] = { + 'isSignal' : 0, + 'isData' : 0, + } + +structure['DY'] = { + 'isSignal' : 0, + 'isData' : 0, + 'removeFromCuts' : ['zh3l_SR_1j'] + } + +structure['Zg'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['ZgS'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['WW'] = { + 'isSignal' : 0, + 'isData' : 0, + 'removeFromCuts' : ['zh3l_SR_1j','zh3l_SR_2j'] + } + +#structure['WWewk'] = { +# 'isSignal' : 0, +# 'isData' : 0 +# } + +#structure['ggWW'] = { +# 'isSignal' : 0, +# 'isData' : 0 +# } + +structure['WZ'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['VVV'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['ZZ'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['ttV'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['top'] = { + 'isSignal' : 0, + 'isData' : 0, + 'removeFromCuts' : ['zh3l_WZ_CR_1j','zh3l_SR_1j'] + } + +# Signal +structure['WH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ZH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ggZH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ttH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['WH_htt'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ZH_htt'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +# Data +structure['DATA'] = { + 'isSignal' : 0, + 'isData' : 1 + } + + + + diff --git a/ZH3l_BDT/zh3l_run2_rdf/valid_data_samples.txt b/ZH3l_BDT/zh3l_run2_rdf/valid_data_samples.txt new file mode 100644 index 00000000..dfc044df --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/valid_data_samples.txt @@ -0,0 +1 @@ +/eos/user/d/dshekar/mkShapesRDF_rootfiles/ZH3l_BDTrun2/rootFile/mkShapes__ZH3l_BDTrun2_manualHADD2.root 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a/ZH3l_BDT/zh3l_run2_rdf/variables.py b/ZH3l_BDT/zh3l_run2_rdf/variables.py new file mode 100644 index 00000000..0ec4ca8a --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_rdf/variables.py @@ -0,0 +1,129 @@ +# variables + +variables = {} + +#'fold' : # 0 = not fold (default), 1 = fold underflowbin, 2 = fold overflow bin, 3 = fold underflow and overflow +# The following is needed if combining plots between years: +#'doWeight' : 1 +#'binX' : 1 +#'binY' : <# of bins> + +variables['events'] = { 'name': '1', + 'range' : (1,0,2), + 'xaxis' : 'events', + 'fold' : 3 + } + +variables['njet'] = { 'name' : 'ZH3l_njet', + 'range' : (10,0,10), + 'xaxis' : 'N_{jet}', + 'fold' : 0 + } + +variables['met'] = { 'name' : 'PuppiMET_pt', + 'range' : (20,0,100), + 'xaxis' : 'Puppi MET', + 'fold' : 0 + } + +variables['pt1'] = { 'name': 'Lepton_pt[0]', + 'range' : (10,0.,200), + 'xaxis' : 'lept1_p_{T} [GeV]', + 'fold' : 0 + } + +variables['dphilmetj'] = { 'name' : 'ZH3l_dphilmetj', #FIXME + 'range' : (16,0,3.14159), + 'xaxis' : 'dphilmetj', + 'fold' : 0 + } + +variables['dphilmetjj'] = { 'name' : 'ZH3l_dphilmetjj', #FIXME + 'range' : (16,0,3.14159), + 'xaxis' : 'dphilmetjj', + 'fold' : 0, + 'cuts' : [cut for cut in cuts if '1j' not in cut] + } + +variables['pTlmetj'] = { 'name' : 'ZH3l_pTlmetj', + 'range' : (20,0,400), + 'xaxis' : 'pTlmetj', + 'fold' : 0 + } + +variables['pTlmetjj'] = { 'name' : 'ZH3l_pTlmetjj', + 'range' : (20,0,400), + 'xaxis' : 'pTlmetjj', + 'fold' : 0, + 'cuts' : [cut for cut in cuts if '1j' not in cut] + } + +variables['mTlmetjj'] = { 'name' : 'ZH3l_mTlmetjj', #FIXME + 'range' : (16,50,450), + 'xaxis' : 'mTlmetjj', + 'fold' : 3, + 'cuts' : [cut for cut in cuts if '1j' not in cut] + } + +variables['mTlmetj'] = { 'name' : 'ZH3l_mTlmetj', #FIXME + 'range' : (10,0,250), + 'xaxis' : 'mTlmetj', + 'fold' : 2, + } + +variables['ptz'] = { 'name' : 'ZH3l_pTZ', + 'range' : (20,0,400), + 'xaxis' : 'ptz', + 'fold' : 0 + } + +variables['mtw_notZ'] = { 'name' : 'ZH3l_mTlmet', #FIXME + 'range' : (20,0,200), + 'xaxis' : 'mTlmet', + 'fold' : 0 + } + +variables['mtw_fit'] = { 'name' : 'ZH3l_mTlmet', #FIXME + 'range' : (8,0,160), + 'xaxis' : 'mTlmet', + 'fold' : 2 + } + +variables['checkmZ'] = { 'name' : 'ZH3l_checkmZ', + 'range' : (20,0,200), + 'xaxis' : 'checkmZ', + 'fold' : 0 + } + +variables['ptjet0'] = { 'name' : 'CleanJet_pt[0]', + 'range' : (20,0,200), + 'xaxis' : 'Leading jet p_{T}', + 'fold' : 0 + } + +variables['ptjet1'] = { 'name' : 'CleanJet_pt[1]', + 'range' : (20,0,200), + 'xaxis' : 'Subleading jet p_{T}', + 'cuts' : [cut for cut in cuts if '1j' not in cut], + 'fold' : 0 + } + +variables['WlepId'] = { 'name' : 'ZH3l_pdgid_l', + 'range' : (31,-15.5,15.5), + 'xaxis' : 'W lepton ID', + 'fold' : 0 + } + +variables['flavor3l'] = { 'name' : '0*(abs(Lepton_pdgId[0])==13 && abs(Lepton_pdgId[1])==13 && abs(Lepton_pdgId[2])==13)+ \ + 1*(abs(Lepton_pdgId[0])==13 && abs(Lepton_pdgId[1])==13 && abs(Lepton_pdgId[2])==11)+ \ + 2*(abs(Lepton_pdgId[0])==13 && abs(Lepton_pdgId[1])==11 && abs(Lepton_pdgId[2])==13)+ \ + 3*(abs(Lepton_pdgId[0])==11 && abs(Lepton_pdgId[1])==13 && abs(Lepton_pdgId[2])==13)+ \ + 4*(abs(Lepton_pdgId[0])==13 && abs(Lepton_pdgId[1])==11 && abs(Lepton_pdgId[2])==11)+ \ + 5*(abs(Lepton_pdgId[0])==11 && abs(Lepton_pdgId[1])==13 && abs(Lepton_pdgId[2])==11)+ \ + 6*(abs(Lepton_pdgId[0])==11 && abs(Lepton_pdgId[1])==11 && abs(Lepton_pdgId[2])==13)+ \ + 7*(abs(Lepton_pdgId[0])==11 && abs(Lepton_pdgId[1])==11 && abs(Lepton_pdgId[2])==11)', + 'range' : (8,-0.5,7.5), + 'xaxis' : 'Trilepton flavor', + 'fold' : 0 + } + From 4afb20e699bbdce80beb4c9fc5688e66061b3255 Mon Sep 17 00:00:00 2001 From: CodexForster Date: Fri, 21 Nov 2025 01:38:35 -0600 Subject: [PATCH 04/13] Z pT reweighting --- ZpTreweighting/aliases.py | 178 ++++++++++++++++++++++++++++++++ ZpTreweighting/configuration.py | 91 ++++++++++++++++ ZpTreweighting/cuts.py | 58 +++++++++++ ZpTreweighting/nuisances.py | 52 ++++++++++ ZpTreweighting/plot.py | 35 +++++++ ZpTreweighting/samples.py | 156 ++++++++++++++++++++++++++++ ZpTreweighting/structure.py | 15 +++ ZpTreweighting/variables.py | 161 +++++++++++++++++++++++++++++ 8 files changed, 746 insertions(+) create mode 100644 ZpTreweighting/aliases.py create mode 100644 ZpTreweighting/configuration.py create mode 100644 ZpTreweighting/cuts.py create mode 100644 ZpTreweighting/nuisances.py create mode 100644 ZpTreweighting/plot.py create mode 100644 ZpTreweighting/samples.py create mode 100644 ZpTreweighting/structure.py create mode 100644 ZpTreweighting/variables.py diff --git a/ZpTreweighting/aliases.py b/ZpTreweighting/aliases.py new file mode 100644 index 00000000..f0c67cfb --- /dev/null +++ b/ZpTreweighting/aliases.py @@ -0,0 +1,178 @@ +import os +import copy +import inspect + +configurations = os.path.realpath(inspect.getfile(inspect.currentframe())) # this file + +aliases = {} +aliases = OrderedDict() + +# Commented out as not needed (DS, 19Nov25) +# mc = [skey for skey in samples if skey not in ('Fake', 'DATA', 'Dyemb', 'DATA_EG', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] +# mc_emb = [skey for skey in samples if skey not in ('Fake', 'DATA', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] + +# LepCut2l__ele_wp90iso__mu_cut_TightID_POG +eleWP = 'mvaWinter22V2Iso_WP90' +muWP = 'cut_Tight_HWW' + +aliases['LepWPCut'] = { + 'expr': 'LepCut2l__ele_'+eleWP+'__mu_'+muWP, + 'samples': mc + ['DATA'], +} + +aliases['LepWPSF'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__mu_'+muWP, + 'samples': mc +} + +# gen-matching to prompt only (GenLepMatch2l matches to *any* gen lepton) +aliases['PromptGenLepMatch2l'] = { + 'expr': 'Alt(Lepton_promptgenmatched, 0, 0) * Alt(Lepton_promptgenmatched, 1, 0)', + 'samples': mc +} + +# Jet bins +# using Alt(CleanJet_pt, n, 0) instead of Sum(CleanJet_pt >= 30) because jet pt ordering is not strictly followed in JES-varied samples + +# No jet with pt > 30 GeV +aliases['zeroJet'] = { + 'expr': 'Alt(CleanJet_pt, 0, 0) < 30.' +} + +aliases['oneJet'] = { + 'expr': 'Alt(CleanJet_pt, 0, 0) > 30.' +} + +aliases['multiJet'] = { + 'expr': 'Alt(CleanJet_pt, 1, 0) > 30.' +} + +aliases['noJetInHorn'] = { + 'expr' : 'Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', +} + +######################################################################## +# B-Tagging WP: https://btv-wiki.docs.cern.ch/ScaleFactors/Run3Summer22/ +######################################################################## + +# Algo / WP / WP cut +btagging_WPs = { + "DeepFlavB" : { + "loose" : "0.0583", + "medium" : "0.3086", + "tight" : "0.7183", + "xtight" : "0.8111", + "xxtight" : "0.9512", + }, + "RobustParTAK4B" : { + "loose" : "0.0849", + "medium" : "0.4319", + "tight" : "0.8482", + "xtight" : "0.9151", + "xxtight" : "0.9874", + }, + "PNetB" : { + "loose" : "0.0470", + "medium" : "0.2450", + "tight" : "0.6734", + "xtight" : "0.7862", + "xxtight" : "0.9610", + } +} + +# Algo / SF name +btagging_SFs = { + "DeepFlavB" : "deepjet", + "RobustParTAK4B" : "partTransformer", + "PNetB" : "partNet", +} + +# Algorithm and WP selection +bAlgo = 'DeepFlavB' # ['DeepFlavB','RobustParTAK4B','PNetB'] +WP = 'loose' # ['loose','medium','tight','xtight','xxtight'] + +# Access information from dictionaries +bWP = btagging_WPs[bAlgo][WP] +bSF = btagging_SFs[bAlgo] + +# B tagging selections and scale factors +aliases['bVeto'] = { + 'expr': f'Sum(CleanJet_pt > 20. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) == 0' +} + +aliases['bVetoSF'] = { + 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>20 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<20 || abs(CleanJet_eta)>2.5))))'.format(bSF), + 'samples': mc +} + +aliases['bReq'] = { + 'expr': f'Sum(CleanJet_pt > 30. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) >= 1' +} + +aliases['bReqSF'] = { + 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>30 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<30 || abs(CleanJet_eta)>2.5))))'.format(bSF), + 'samples': mc +} + +# Top control region +aliases['topcr'] = { + 'expr': 'mtw2>30 && mll>50 && ((zeroJet && !bVeto) || bReq)' +} + +# WW control region +aliases['wwcr'] = { + 'expr': 'mth>60 && mtw2>30 && mll>100 && bVeto' +} + +# Overall b tag SF +aliases['btagSF'] = { + 'expr': '(bVeto || (topcr && zeroJet))*bVetoSF + (topcr && !zeroJet)*bReqSF', + 'samples': mc +} + +# Systematic uncertainty variations +for shift in ['jes','lf','hf','lfstats1','lfstats2','hfstats1','hfstats2','cferr1','cferr2']: + + for targ in ['bVeto', 'bReq']: + alias = aliases['%sSF%sup' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) + alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_up_%s' % shift) + + alias = aliases['%sSF%sdown' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) + alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_down_%s' % shift) + + aliases['btagSF%sup' % shift] = { + 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'up'), + 'samples': mc + } + + aliases['btagSF%sdown' % shift] = { + 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'down'), + 'samples': mc + } + +########################################################################## +# End of b tagging +########################################################################## + +# Data/MC scale factors and systematic uncertainties +aliases['SFweight'] = { + 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF','btagSF']), + 'samples': mc +} + +aliases['SFweightEleUp'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__Up', + 'samples': mc +} +aliases['SFweightEleDown'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__Down', + 'samples': mc +} +aliases['SFweightMuUp'] = { + 'expr': 'LepSF2l__mu_'+muWP+'__Up', + 'samples': mc +} +aliases['SFweightMuDown'] = { + 'expr': 'LepSF2l__mu_'+muWP+'__Down', + 'samples': mc +} diff --git a/ZpTreweighting/configuration.py b/ZpTreweighting/configuration.py new file mode 100644 index 00000000..6ffa9224 --- /dev/null +++ b/ZpTreweighting/configuration.py @@ -0,0 +1,91 @@ +import sys,os + +# tag used to identify the configuration folder version +tag = "ZpTreweighting" + +# file to use as runner script, default uses mkShapesRDF.shapeAnalysis.runner, otherwise specify path to script +runnerFile = "default" + +# output file name +outputFile = "mkShapes__{}.root".format(tag) + +# path to ouput folder +outputFolder = "/eos/user/" + os.getlogin()[0] + "/" + os.getlogin() + "/mkShapesRDF_rootfiles/" + tag + "/rootFile/" + +# path to batch folder (used for condor submission) +batchFolder = "condor" + +# path to configuration folder (will contain all the compiled configuration files) +configsFolder = "configs" + +# luminosity to normalize to (in 1/fb) +# https://github.com/latinos/mkShapesRDF/blob/Run3/mkShapesRDF/processor/data/TrigMaker_cfg.py#L1016 +lumi = 8.174732641 + +# file with dict of aliases to define +aliasesFile = "aliases.py" + +# file with dict of variables +variablesFile = "variables.py" + +# file with dict of cuts +cutsFile = "cuts.py" + +# file with dict of samples +samplesFile = "samples.py" + +# file with dict of samples +plotFile = "plot.py" + +# file with dict of structure (used to define combine processes) +structureFile = "structure.py" + +# nuisances file for mkDatacards and for mkShape +nuisancesFile = "nuisances.py" + +# path to folder where to save plots +plotPath = "plots_" + tag + +# this lines are executed right before the runner on the condor node +mountEOS = [ + # "export KRB5CCNAME=/home/gpizzati/krb5\n", +] + +# list of imports to import when compiling the whole configuration folder, it should not contain imports used by configuration.py +imports = ["os", "glob", ("collections", "OrderedDict"), "ROOT"] + +# list of files to compile +filesToExec = [ + samplesFile, + aliasesFile, + variablesFile, + cutsFile, + plotFile, + nuisancesFile, + structureFile, +] + +# list of variables to keep in the compiled configuration folder +varsToKeep = [ + "batchVars", + "outputFolder", + "batchFolder", + "configsFolder", + "outputFile", + "runnerFile", + "tag", + "samples", + "aliases", + "variables", + ("cuts", {"cuts": "cuts", "preselections": "preselections"}), + ("plot", {"plot": "plot", "groupPlot": "groupPlot", "legend": "legend"}), + "nuisances", + "structure", + "lumi", +] + +# list of variables to keep in the batch submission script (script.py) +batchVars = varsToKeep[varsToKeep.index("samples") :] + + +varsToKeep += ['plotPath'] diff --git a/ZpTreweighting/cuts.py b/ZpTreweighting/cuts.py new file mode 100644 index 00000000..35f4df1e --- /dev/null +++ b/ZpTreweighting/cuts.py @@ -0,0 +1,58 @@ +cuts = {} + +# Preselections - applied to all the cuts, noJetInHorn replaced by zeroJet +preselections = 'Lepton_pt[0] > 25 \ + && Lepton_pt[1] > 13 \ + && (nLepton >= 2 && Alt(Lepton_pt,2,0) < 10) \ + && abs(Lepton_eta[0]) < 2.5 \ + && abs(Lepton_eta[1]) < 2.5 \ + && mll > 60 \ + && mll < 120 \ + && zeroJet \ +' + +# Individual cuts and categories + +# Commenting out the inclusive cuts (DS, 19Nov25) +# cuts['Zee_incl'] = '(Lepton_pdgId[0] * Lepton_pdgId[1] == -11*11)' +# cuts['Zmm_incl'] = '(Lepton_pdgId[0] * Lepton_pdgId[1] == -13*13)' + +cuts['Zee'] = { + 'expr' : '(Lepton_pdgId[0] * Lepton_pdgId[1] == -11*11)', + 'categories' : { + '0j' : 'zeroJet', + '1j' : 'oneJet && Alt(CleanJet_pt,1,0)<30', + '2j' : 'multiJet', + } +} + +cuts['Zmm'] = { + 'expr' : '(Lepton_pdgId[0] * Lepton_pdgId[1] == -13*13)', + 'categories' : { + '0j' : 'zeroJet', + '1j' : 'oneJet && Alt(CleanJet_pt,1,0)<30', + '2j' : 'multiJet', + } +} + +# cuts['Zee_noJetInHorn_incl'] = '(Lepton_pdgId[0] * Lepton_pdgId[1] == -11*11) && Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0' + +# cuts['Zmm_noJetInHorn_incl'] = '(Lepton_pdgId[0] * Lepton_pdgId[1] == -13*13) && Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0' + +# cuts['Zee_noJetInHorn'] = { +# 'expr' : '(Lepton_pdgId[0] * Lepton_pdgId[1] == -11*11) && Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', +# 'categories' : { +# '0j' : 'zeroJet', +# '1j' : 'oneJet && Alt(CleanJet_pt,1,0)<30', +# '2j' : 'multiJet', +# } +# } + +# cuts['Zmm_noJetInHorn'] = { +# 'expr' : '(Lepton_pdgId[0] * Lepton_pdgId[1] == -13*13) && Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', +# 'categories' : { +# '0j' : 'zeroJet', +# '1j' : 'oneJet && Alt(CleanJet_pt,1,0)<30', +# '2j' : 'multiJet', +# } +# } diff --git a/ZpTreweighting/nuisances.py b/ZpTreweighting/nuisances.py new file mode 100644 index 00000000..17c37734 --- /dev/null +++ b/ZpTreweighting/nuisances.py @@ -0,0 +1,52 @@ +print(treeBaseDir) +def makeMCDirectory(var=''): + _treeBaseDir = treeBaseDir + '' + if useXROOTD: + _treeBaseDir = redirector + treeBaseDir + if var== '': + return '/'.join([_treeBaseDir, mcProduction, mcSteps]) + else: + return '/'.join([_treeBaseDir, mcProduction, mcSteps + '__' + var]) + + +# merge cuts +_mergedCuts = [] +for cut in list(cuts.keys()): + __cutExpr = '' + if type(cuts[cut]) == dict: + __cutExpr = cuts[cut]['expr'] + for cat in list(cuts[cut]['categories'].keys()): + _mergedCuts.append(cut + '_' + cat) + elif type(cuts[cut]) == str: + _mergedCuts.append(cut) + + +# Dfinitions of groups of samples +mc = [skey for skey in samples if skey not in ('DATA')] + +nuisances = {} + + +################################ EXPERIMENTAL UNCERTAINTIES ################################# + +#### Luminosity + +# https://twiki.cern.ch/twiki/bin/view/CMS/LumiRecommendationsRun3 +nuisances['lumi_2022'] = { + 'name' : 'lumi_2022', + 'type' : 'lnN', + 'samples' : dict((skey, '1.014') for skey in mc) +} + +### MC statistical uncertainty +autoStats = True +if autoStats: + ## Use the following if you want to apply the automatic combine MC stat nuisances. + nuisances['stat'] = { + 'type': 'auto', + 'maxPoiss': '10', + 'includeSignal': '0', + # nuisance ['maxPoiss'] = Number of threshold events for Poisson modelling + # nuisance ['includeSignal'] = Include MC stat nuisances on signal processes (1=True, 0=False) + 'samples': {} + } diff --git a/ZpTreweighting/plot.py b/ZpTreweighting/plot.py new file mode 100644 index 00000000..9fa555cd --- /dev/null +++ b/ZpTreweighting/plot.py @@ -0,0 +1,35 @@ +# Group plot +groupPlot = {} + +groupPlot['DY'] = { + 'nameHR' : 'DY', + 'isSignal' : 0, + 'color' : 420, # kGreen+4 + 'samples' : ['DY'] +} + + +# Plot +plot = {} + +plot['DY'] = { + 'color' : 420, # kGreen+4 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + + +# Data +plot['DATA'] = { + 'nameHR' : 'Data', + 'color' : 1 , + 'isSignal' : 0, + 'isData' : 1 , + 'isBlind' : 0 +} + +# Legend definition +legend = {} +legend['lumi'] = 'L = 8.2 fb^{-1}' +legend['sqrt'] = '#sqrt{s} = 13.6 TeV' diff --git a/ZpTreweighting/samples.py b/ZpTreweighting/samples.py new file mode 100644 index 00000000..4216032c --- /dev/null +++ b/ZpTreweighting/samples.py @@ -0,0 +1,156 @@ +from mkShapesRDF.lib.search_files import SearchFiles + +searchFiles = SearchFiles() + +redirector = "" +useXROOTD = False + +# MC: /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight +# DATA: /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight + +mcProduction = 'Summer22_130x_nAODv12_Full2022v12' +mcSteps = 'MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight' +dataReco = 'Run2022_ReReco_nAODv12_Full2022v12' +dataSteps = 'DATAl2loose2022v12__l2tight' +# fakeSteps = 'DATAl1loose2022EFGv12__fakeW' + +############################################## +###### Tree base directory for the site ###### +############################################## +treeBaseDir = '/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano' +limitFiles = -1 + +def makeMCDirectory(var=""): + _treeBaseDir = treeBaseDir + "" + if redirector != "": + _treeBaseDir = redirector + treeBaseDir + if var == "": + return "/".join([_treeBaseDir, mcProduction, mcSteps]) + else: + return "/".join([_treeBaseDir, mcProduction, mcSteps + "__" + var]) + + +mcDirectory = makeMCDirectory() +# fakeDirectory = os.path.join(treeBaseDir, dataReco, fakeSteps) +dataDirectory = os.path.join(treeBaseDir, dataReco, dataSteps) + +samples = {} + + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + return [(name, _files[:limitFiles])] + else: + return [(name, _files)] + + +def CombineBaseW(samples, proc, samplelist): + _filtFiles = list(filter(lambda k: k[0] in samplelist, samples[proc]["name"])) + _files = list(map(lambda k: k[1], _filtFiles)) + _l = list(map(lambda k: len(k), _files)) + leastFiles = _files[_l.index(min(_l))] + dfSmall = ROOT.RDataFrame("Runs", leastFiles) + s = dfSmall.Sum("genEventSumw").GetValue() + f = ROOT.TFile(leastFiles[0]) + t = f.Get("Events") + t.GetEntry(1) + xs = t.baseW * s + + __files = [] + for f in _files: + __files += f + df = ROOT.RDataFrame("Runs", __files) + s = df.Sum("genEventSumw").GetValue() + newbaseW = str(xs / s) + weight = newbaseW + "/baseW" + + for iSample in samplelist: + addSampleWeight(samples, proc, iSample, weight) + + +def addSampleWeight(samples, sampleName, sampleNameType, weight): + obj = list(filter(lambda k: k[0] == sampleNameType, samples[sampleName]["name"]))[0] + samples[sampleName]["name"] = list( + filter(lambda k: k[0] != sampleNameType, samples[sampleName]["name"]) + ) + if len(obj) > 2: + samples[sampleName]["name"].append( + (obj[0], obj[1], obj[2] + "*(" + weight + ")") + ) + else: + samples[sampleName]["name"].append((obj[0], obj[1], "(" + weight + ")")) + + +################################################ +############ DATA DECLARATION ################## +################################################ + +DataRun = [ + ['B','Run2022B-ReReco-v1'], + ['C','Run2022C-ReReco-v1'], + ['D','Run2022D-ReReco-v1'], +] + +# ['E','Run2022E-Prompt-v1'], +# ['F','Run2022F-Prompt-v1'], +# ['G','Run2022G-Prompt-v1'], + +DataSets = ['MuonEG','SingleMuon','Muon','EGamma'] + +# Proceeding with no HLT selection at the moment (DS, 19Nov25) +DataTrig = { + 'MuonEG' : ' Trigger_ElMu' , + 'SingleMuon' : '!Trigger_ElMu && Trigger_sngMu' , + 'Muon' : '!Trigger_ElMu && (Trigger_sngMu || Trigger_dblMu)', + 'EGamma' : '!Trigger_ElMu && !Trigger_sngMu && !Trigger_dblMu && (Trigger_sngEl || Trigger_dblEl)' +} + + +######################################### +############ MC COMMON ################## +######################################### + +# SFweight does not include btag weights +mcCommonWeightNoMatch = 'XSWeight*METFilter_Common*SFweight' +mcCommonWeight = 'XSWeight*METFilter_Common*PromptGenLepMatch2l*SFweight' + +#mcCommonWeight = 'XSWeight*METFilter_Common*SFweight' + +########################################### +############# BACKGROUNDS ############### +########################################### + +# DY +files = nanoGetSampleFiles(mcDirectory, 'DYto2L-2Jets_MLL-50') + +samples['DY'] = { + 'name': files, + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + +########################################### +################## DATA ################### +########################################### + +samples['DATA'] = { + 'name': [], + 'weight': 'LepWPCut*METFilter_DATA', + 'weights': [], + 'isData': ['all'], + 'FilesPerJob': 15 +} + +for _, sd in DataRun: + for pd in DataSets: + datatag = pd + '_' + sd + + if (pd == "SingleMuon" and _ in ["D"]) or (pd == "Muon" and _ == "B"): + continue + files = nanoGetSampleFiles(dataDirectory, datatag) + + print(datatag) + + samples['DATA']['name'].extend(files) + addSampleWeight(samples, 'DATA', datatag, DataTrig[pd]) diff --git a/ZpTreweighting/structure.py b/ZpTreweighting/structure.py new file mode 100644 index 00000000..508334d3 --- /dev/null +++ b/ZpTreweighting/structure.py @@ -0,0 +1,15 @@ +# structure configuration for datacard + +structure = {} + +# keys here must match keys in samples.py +structure['DY'] = { + 'isSignal' : 0, + 'isData' : 0 +} + +# data +structure['DATA'] = { + 'isSignal' : 0, + 'isData' : 1 +} diff --git a/ZpTreweighting/variables.py b/ZpTreweighting/variables.py new file mode 100644 index 00000000..27426716 --- /dev/null +++ b/ZpTreweighting/variables.py @@ -0,0 +1,161 @@ +# variables +variables = {} + +variables['events'] = { + 'name' : '1', + 'range' : (1,0,2), + 'xaxis' : 'events', + 'fold' : 3 +} + +variables['nvtx'] = { + 'name' : 'PV_npvsGood', + 'range' : (100, 0, 100), + 'xaxis' : 'number of vertices', + 'fold' : 3 +} + +variables['mll'] = { + 'name': 'mll', + 'range' : (50,0,200), + 'xaxis' : 'm_{ll} [GeV]', + 'fold' : 0 +} + +variables['ptll'] = { + 'name': 'ptll', + 'range' : (40,0,200), + 'xaxis' : 'p_{T}^{ll} [GeV]', + 'fold' : 0 +} + +variables['drll'] = { + 'name': 'drll', + 'range' : (50, 0,5), + 'xaxis' : '#Delta R_{ll}', + 'fold' : 0 +} + +variables['dphill'] = { + 'name': 'dphill', + 'range' : (50,0,5), + 'xaxis' : '#Delta #phi_{ll}', + 'fold' : 0 +} + +variables['pt1'] = { + 'name': 'Lepton_pt[0]', + 'range' : (40,0,200), + 'xaxis' : 'p_{T} 1st lep', + 'fold' : 3 +} + +variables['pt2'] = { + 'name': 'Lepton_pt[1]', + 'range' : (40,0,160), + 'xaxis' : 'p_{T} 2nd lep', + 'fold' : 3 +} + +variables['eta1'] = { + 'name': 'Lepton_eta[0]', + 'range' : (50,-2.5,2.5), + 'xaxis' : '#eta 1st lep', + 'fold' : 3 +} + +variables['eta2'] = { + 'name': 'Lepton_eta[1]', + 'range' : (50,-2.5,2.5), + 'xaxis' : '#eta 2nd lep', + 'fold' : 3 +} + + +# B Tag +variables['jetdeepb'] = { + 'name': 'Alt(Take(Jet_btagDeepFlavB, CleanJet_jetIdx), 0, -99)', + 'range' : (40,-1,1), + 'xaxis' : 'B tagger 1st jet (DeepB)', + 'fold' : 2 +} + +variables['jetParT'] = { + 'name': 'Alt(Take(Jet_btagRobustParTAK4B, CleanJet_jetIdx), 0, -99)', + 'range' : (40,-1,1), + 'xaxis' : 'B tagger 1st jet (RobustParT AK4B)', + 'fold' : 2 +} + +variables['jetPNetB'] = { + 'name': 'Alt(Take(Jet_btagPNetB, CleanJet_jetIdx), 0, -99)', + 'range' : (40,-1,1), + 'xaxis' : 'B tagger 1st jet (ParticleNet B)', + 'fold' : 2 +} + + +# MET +variables['trkMet'] = { + 'name': 'TkMET_pt', + 'range' : (20,0,200), + 'xaxis' : 'trk met [GeV]', + 'fold' : 3 +} + +variables['puppimet'] = { + 'name': 'PuppiMET_pt', + 'range' : (20,0,200), + 'xaxis' : 'Puppi MET p_{T} [GeV]', + 'fold' : 3 +} + +############# New Jet processing +variables['njet'] = { + 'name': 'Sum(CleanJet_pt>30)', + 'range' : (5,0,5), + 'xaxis' : 'Number of jets', + 'fold' : 2 +} + +variables['jetpt1'] = { + 'name': 'Alt(CleanJet_pt, 0, -99) - 9999.9*(CleanJet_pt[0]<30)', + 'range' : (40,0,200), + 'xaxis' : 'p_{T} 1st jet', + 'fold' : 0 +} + +variables['jetpt2'] = { + 'name': 'Alt(CleanJet_pt, 1, -99) - 9999.9*(CleanJet_pt[1]<30)', + 'range' : (40,0,200), + 'xaxis' : 'p_{T} 2nd jet', + 'fold' : 0 +} + +variables['jeteta1'] = { + 'name': 'Alt(CleanJet_eta, 0, -99) - 9999.9*(CleanJet_pt[0]<30)', + 'range' : (30,-4.7,4.7), + 'xaxis' : '#eta 1st jet', + 'fold' : 0 +} + +variables['jeteta1_fine_binning'] = { + 'name': 'Alt(CleanJet_eta, 0, -99) - 9999.9*(CleanJet_pt[0]<30)', + 'range' : (94,-4.7,4.7), + 'xaxis' : '#eta 1st jet', + 'fold' : 0 +} + +variables['jeteta2'] = { + 'name': 'Alt(CleanJet_eta, 1, -99) - 9999.9*(CleanJet_pt[1]<30)', + 'range' : (30,-4.7,4.7), + 'xaxis' : '#eta 2nd jet', + 'fold' : 0 +} + +variables['jeteta2_fine_binning'] = { + 'name': 'Alt(CleanJet_eta, 1, -99) - 9999.9*(CleanJet_pt[1]<30)', + 'range' : (94,-4.7,4.7), + 'xaxis' : '#eta 2nd jet', + 'fold' : 0 +} From cec97c4f0a573cb2db94828282f0c633348090b5 Mon Sep 17 00:00:00 2001 From: CodexForster Date: Sun, 7 Dec 2025 22:50:48 -0600 Subject: [PATCH 05/13] ptll plots, bg subtraction from data, rw factor fits --- ZpTreweighting/aliases.py | 115 ++++++++------- ZpTreweighting/extract_Zptrw.py | 128 ++++++++++++++++ ZpTreweighting/genptll_vs_ptll.cc | 234 ++++++++++++++++++++++++++++++ ZpTreweighting/plot.py | 32 ++++ ZpTreweighting/readme.md | 6 + ZpTreweighting/samples.py | 70 ++++++++- ZpTreweighting/structure.py | 15 ++ ZpTreweighting/variables.py | 17 ++- 8 files changed, 558 insertions(+), 59 deletions(-) create mode 100644 ZpTreweighting/extract_Zptrw.py create mode 100644 ZpTreweighting/genptll_vs_ptll.cc create mode 100644 ZpTreweighting/readme.md diff --git a/ZpTreweighting/aliases.py b/ZpTreweighting/aliases.py index f0c67cfb..12aee286 100644 --- a/ZpTreweighting/aliases.py +++ b/ZpTreweighting/aliases.py @@ -7,8 +7,8 @@ aliases = {} aliases = OrderedDict() -# Commented out as not needed (DS, 19Nov25) -# mc = [skey for skey in samples if skey not in ('Fake', 'DATA', 'Dyemb', 'DATA_EG', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] +mc = [skey for skey in samples if skey not in ('Fake', 'DATA', 'Dyemb', 'DATA_EG', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] +# Commented out as not used (DS, 19Nov25) # mc_emb = [skey for skey in samples if skey not in ('Fake', 'DATA', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] # LepCut2l__ele_wp90iso__mu_cut_TightID_POG @@ -31,6 +31,15 @@ 'samples': mc } +aliases['gen_Zpt'] = { + # 'linesToAdd': [".L /afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+"], + 'linesToAdd': ['#include "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc"'], + 'class': 'GetGenZpt', + 'args': 'nGenPart, GenPart_pt, GenPart_pdgId, GenPart_genPartIdxMother, GenPart_statusFlags, gen_ptll', + # 'expr': 'gen_ptll', + 'samples': mc +} + # Jet bins # using Alt(CleanJet_pt, n, 0) instead of Sum(CleanJet_pt >= 30) because jet pt ordering is not strictly followed in JES-varied samples @@ -95,71 +104,77 @@ bWP = btagging_WPs[bAlgo][WP] bSF = btagging_SFs[bAlgo] -# B tagging selections and scale factors -aliases['bVeto'] = { - 'expr': f'Sum(CleanJet_pt > 20. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) == 0' -} +# # B tagging selections and scale factors +if dataset_samples == 'amassiro': + aliases['bVeto'] = { + 'expr': f'Sum(CleanJet_pt > 20. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) == 0' + } -aliases['bVetoSF'] = { - 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>20 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<20 || abs(CleanJet_eta)>2.5))))'.format(bSF), - 'samples': mc -} + aliases['bVetoSF'] = { + 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>20 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<20 || abs(CleanJet_eta)>2.5))))'.format(bSF), + 'samples': mc + } -aliases['bReq'] = { - 'expr': f'Sum(CleanJet_pt > 30. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) >= 1' -} + aliases['bReq'] = { + 'expr': f'Sum(CleanJet_pt > 30. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) >= 1' + } -aliases['bReqSF'] = { - 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>30 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<30 || abs(CleanJet_eta)>2.5))))'.format(bSF), - 'samples': mc -} + aliases['bReqSF'] = { + 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>30 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<30 || abs(CleanJet_eta)>2.5))))'.format(bSF), + 'samples': mc + } -# Top control region -aliases['topcr'] = { - 'expr': 'mtw2>30 && mll>50 && ((zeroJet && !bVeto) || bReq)' -} + # Top control region + aliases['topcr'] = { + 'expr': 'mtw2>30 && mll>50 && ((zeroJet && !bVeto) || bReq)' + } -# WW control region -aliases['wwcr'] = { - 'expr': 'mth>60 && mtw2>30 && mll>100 && bVeto' -} + # WW control region + aliases['wwcr'] = { + 'expr': 'mth>60 && mtw2>30 && mll>100 && bVeto' + } -# Overall b tag SF -aliases['btagSF'] = { - 'expr': '(bVeto || (topcr && zeroJet))*bVetoSF + (topcr && !zeroJet)*bReqSF', - 'samples': mc -} + # Overall b tag SF + aliases['btagSF'] = { + 'expr': '(bVeto || (topcr && zeroJet))*bVetoSF + (topcr && !zeroJet)*bReqSF', + 'samples': mc + } -# Systematic uncertainty variations -for shift in ['jes','lf','hf','lfstats1','lfstats2','hfstats1','hfstats2','cferr1','cferr2']: + # Systematic uncertainty variations + for shift in ['jes','lf','hf','lfstats1','lfstats2','hfstats1','hfstats2','cferr1','cferr2']: - for targ in ['bVeto', 'bReq']: - alias = aliases['%sSF%sup' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) - alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_up_%s' % shift) + for targ in ['bVeto', 'bReq']: + alias = aliases['%sSF%sup' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) + alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_up_%s' % shift) - alias = aliases['%sSF%sdown' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) - alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_down_%s' % shift) + alias = aliases['%sSF%sdown' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) + alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_down_%s' % shift) - aliases['btagSF%sup' % shift] = { - 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'up'), - 'samples': mc - } + aliases['btagSF%sup' % shift] = { + 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'up'), + 'samples': mc + } - aliases['btagSF%sdown' % shift] = { - 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'down'), - 'samples': mc - } + aliases['btagSF%sdown' % shift] = { + 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'down'), + 'samples': mc + } ########################################################################## # End of b tagging ########################################################################## # Data/MC scale factors and systematic uncertainties -aliases['SFweight'] = { - 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF','btagSF']), - 'samples': mc -} - +if dataset_samples == 'amassiro': + aliases['SFweight'] = { + 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF','btagSF']), + 'samples': mc + } +elif dataset_samples == 'calderon': + aliases['SFweight'] = { + 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF','1']), # Remove btagSF for samples from Calderon (DS, 22Nov25) + 'samples': mc + } aliases['SFweightEleUp'] = { 'expr': 'LepSF2l__ele_'+eleWP+'__Up', 'samples': mc diff --git a/ZpTreweighting/extract_Zptrw.py b/ZpTreweighting/extract_Zptrw.py new file mode 100644 index 00000000..7cd229ea --- /dev/null +++ b/ZpTreweighting/extract_Zptrw.py @@ -0,0 +1,128 @@ +import ROOT +import mplhep as hep +import matplotlib.pyplot as plt +import numpy as np + +# CMS plot style +hep.style.use("CMS") +style = hep.style.CMS +style["font.size"] = 13 +plt.style.use(style) + +root_file = ROOT.TFile("mkShapes__ZpTreweighting.root") +zee_dir = root_file.Get("Zee_0j") +ptll_dir = zee_dir.Get("ptll") + +histo_DY = ptll_dir.Get("histo_DY") +histo_DATA = ptll_dir.Get("histo_DATA") +histo_top = ptll_dir.Get("histo_top") +histo_diboson = ptll_dir.Get("histo_diboson") +histo_SMhiggs = ptll_dir.Get("histo_SMhiggs") + +# Subtract DY backgrounds from DATA +histo_trueData = histo_DATA.Clone("histo_trueData") # Create a clone for the result +histo_trueData.Add(histo_top, -1) +histo_trueData.Add(histo_diboson, -1) +histo_trueData.Add(histo_SMhiggs, -1) + +# Create a ratio plot of DATA to DY +histo_ratio = histo_trueData.Clone("histo_ratio") +histo_ratio.Divide(histo_DY) + +c = ROOT.TCanvas("c", "c", 800, 800) +c.Divide(1,2) + +# Top pad: main plot +pad1 = c.cd(1) +pad1.SetPad(0.0, 0.30, 1.0, 1.0) +pad1.SetBottomMargin(0.02) +pad1.SetLogy() # if you want log-y + +histo_DY.SetTitle("") +histo_DY.GetYaxis().SetTitle("Events / 5 GeV") +histo_DY.GetXaxis().SetLabelSize(0) +histo_DY.GetYaxis().SetTitleSize(0.06) +histo_DY.GetYaxis().SetTitleOffset(1.1) +histo_DY.GetYaxis().SetLabelSize(0.05) + +# Draw DY as reference, then data points +histo_DY.SetLineColor(ROOT.kBlue) +histo_DY.SetStats(False) +histo_DY.SetFillColorAlpha(ROOT.kBlue, 0.1) +histo_DY.Draw("HIST") +histo_trueData.SetMarkerColor(ROOT.kBlack) # Change marker color to red +histo_trueData.SetMarkerSize(1.3) # Change marker color to red +histo_trueData.Draw("E1 SAME") + +# CMS label +label = ROOT.TLatex() +label.SetNDC(True) +label.SetTextSize(0.040) +label.DrawLatex(0.12, 0.92, "#bf{CMS} #it{Preliminary}") +label.DrawLatex(0.55, 0.92, "#sqrt{s} = 13.6 TeV, L_{int} = 8.2 fb^{-1}") + +leg = ROOT.TLegend(0.60, 0.70, 0.88, 0.88) +leg.SetBorderSize(0) +leg.SetFillStyle(0) +leg.AddEntry(histo_trueData, "Data", "pe") +leg.AddEntry(histo_DY, "DY", "f") +leg.Draw() + +# Bottom pad: ratio +pad2 = c.cd(2) +pad2.SetPad(0.0, 0.0, 1.0, 0.30) +pad2.SetTopMargin(0.02) +pad2.SetBottomMargin(0.35) + +h_ratio_points = histo_ratio.Clone("h_ratio_points") +h_ratio_points.SetTitle("") +h_ratio_points.SetMarkerStyle(20) +h_ratio_points.SetMarkerSize(0.8) +h_ratio_points.SetLineColor(ROOT.kBlack) +h_ratio_points.SetMarkerColor(ROOT.kBlack) + +h_ratio_points.GetYaxis().SetTitle("Data/DY") +h_ratio_points.GetYaxis().SetNdivisions(505) +h_ratio_points.GetYaxis().SetTitleSize(0.10) +h_ratio_points.GetYaxis().SetTitleOffset(0.45) +h_ratio_points.GetYaxis().SetLabelSize(0.08) +h_ratio_points.GetXaxis().SetTitle("p_{T}^{ll} [GeV]") +h_ratio_points.GetXaxis().SetTitleSize(0.12) +h_ratio_points.GetXaxis().SetLabelSize(0.10) + +h_ratio_points.SetMinimum(0.0) +h_ratio_points.SetMaximum(2.0) + +h_ratio_points.Draw("E1") + +# horizontal line at 1 +line = ROOT.TLine(h_ratio_points.GetXaxis().GetXmin(), 1.0, + h_ratio_points.GetXaxis().GetXmax(), 1.0) +line.SetLineColor(ROOT.kGray+2) +line.SetLineStyle(2) +line.Draw("SAME") + +# --- Fit the ratio with an error function --- +# erf(x; p0, p1, p2, p3) = p0 + p1 * TMath::Erf((x - p2)/p3) +fit_func = ROOT.TF1("fit_erf", + "[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x*x + [6])", + histo_ratio.GetXaxis().GetXmin(), + histo_ratio.GetXaxis().GetXmax()) +fit_func.SetParameters(1.0, 0.05, 10.0, 5.0, 0.0, 0.0, 1.0) # reasonable starting values +fit_func.SetLineColor(ROOT.kRed) + +h_ratio_points.Fit(fit_func, "R") # fit only in the visible x-range +fit_func.Draw("SAME") + +c.SaveAs("ZpTreweighting_main_plus_ratio.pdf") +c.SaveAs("ZpTreweighting_main_plus_ratio.png") + +# --- Optional: save the ratio+fit alone in a separate figure --- +c_ratio_only = ROOT.TCanvas("c_ratio_only", "c_ratio_only", 800, 800) +h_ratio_points.Draw("E1") +h_ratio_points.GetYaxis().SetTitle("Data/DY") +h_ratio_points.GetXaxis().SetTitle("p_{T}^{ll} [GeV]") +line.Draw("SAME") +fit_func.Draw("SAME") +c_ratio_only.SaveAs("ZpTreweighting_ratio_fit.pdf") +c_ratio_only.SaveAs("ZpTreweighting_ratio_fit.png") \ No newline at end of file diff --git a/ZpTreweighting/genptll_vs_ptll.cc b/ZpTreweighting/genptll_vs_ptll.cc new file mode 100644 index 00000000..37fd995c --- /dev/null +++ b/ZpTreweighting/genptll_vs_ptll.cc @@ -0,0 +1,234 @@ +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +#include + +#include "TH1.h" +#include "TH2.h" +#include "TFile.h" +#include "TTree.h" +#include "TString.h" +#include "TSystem.h" +#include "TROOT.h" +#include "TObjString.h" +#include "TChain.h" +#include "TCut.h" +#include "TGraphAsymmErrors.h" +#include "TLorentzVector.h" +#include "TLine.h" +#include "TCanvas.h" +#include +#include + + +struct dataset { + int year; + std::map samples; + std::map> algo; +}; + +dataset mkDataset(int year) { + TH1::SetDefaultSumw2(true); + dataset d; + d.year = year; + if (year == 2022) { + d.samples = { + {"DY" , "/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Summer22_130x_nAODv12_Full2022v12_OLD/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/nanoLatino_DYto2L-2Jets_MLL-50__part*.root"} + }; + d.algo = { + {"DeepFlavB", {{"loose" , 0.0583}, {"medium" , 0.3086}, {"tight" , 0.7183}}}, + {"RobustParTAK4B", {{"loose", 0.0849}, {"medium", 0.4319}, {"tight", 0.8482}}}, + {"PNetB", {{"loose", 0.0470}, {"medium", 0.2450}, {"tight", 0.6734}}} + }; + } + + for (auto const &elem : d.algo) { + std::cout << elem.first << "\n"; + auto const &inner_map = elem.second; + for (auto const [key, value] : inner_map) + { + std::cout << key << value << "\n"; + } + } + return d; +} + + +void genptll_vs_ptll( + int year = 2022, + std::string process = "DY", + std::string algo = "DeepFlavB", + std::string const WP = "loose" +){ + dataset d = mkDataset(year); + std::cout << d.samples[process] << "\n"; + std::cout << d.algo[algo][WP] << "\n"; + double wp = d.algo[algo][WP]; + + TH1::SetDefaultSumw2(true); + TString fname = "pT2Dhist_" + std::to_string(year) + "_" + process + "_" + algo + "_" + WP + ".root"; + TString samples = d.samples[process]; + TFile* outfile = new TFile(fname, "RECREATE"); + + TChain *Events = new TChain("Events"); + Events->Add(samples); + + Events->SetBranchStatus("*", 0); + Events->SetBranchStatus("XSWeight", 1); + Events->SetBranchStatus("nCleanJet", 1); + Events->SetBranchStatus("nLepton", 1); + Events->SetBranchStatus("Lepton_pt", 1); + Events->SetBranchStatus("Lepton_eta", 1); + Events->SetBranchStatus("Lepton_pdgId", 1); + Events->SetBranchStatus("mll", 1); + Events->SetBranchStatus("ptll", 1); + Events->SetBranchStatus("gen_ptll", 1); + + double XSWeight; + Int_t nCleanJet; + Int_t nLepton; + float Lepton_pt[100]; + float Lepton_eta[100]; + int Lepton_pdgId[100]; + Double_t mll, ptll; + float gen_ptll; + + Events->SetBranchAddress("XSWeight", &XSWeight); + Events->SetBranchAddress("nCleanJet", &nCleanJet); + Events->SetBranchAddress("nLepton", &nLepton); + Events->SetBranchAddress("Lepton_pt", Lepton_pt); + Events->SetBranchAddress("Lepton_eta", Lepton_eta); + Events->SetBranchAddress("Lepton_pdgId", Lepton_pdgId); + Events->SetBranchAddress("mll", &mll); + Events->SetBranchAddress("ptll", &ptll); + Events->SetBranchAddress("gen_ptll", &gen_ptll); + + + /* + ptbins follows the BTV recommendation + https://btv-wiki.docs.cern.ch/PerformanceCalibration/fixedWPSFRecommendations/#b-tagging-efficiencies-in-simulation + etabins can be changed to match one's needs + */ + + Float_t ptbins[11] = {0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100}; + + TH1F *ptll_pull = new TH1F{"ptll_pull", "ptll_pull", 100, -2, 2}; + ptll_pull->GetXaxis()->SetTitle("(reco ptll - gen ptll)/gen ptll"); + + TH2F *genReco_pT_0j = new TH2F{"gen_vs_reco_pt_0j", "gen_vs_reco_pt_0j", 10, ptbins, 10, ptbins}; + genReco_pT_0j->GetYaxis()->SetTitle("p_{T} [GeV]"); + genReco_pT_0j->GetXaxis()->SetTitle("gen p_{T} [GeV]"); + + TH2F *genReco_pT_1j = new TH2F{"gen_vs_reco_pt_1j", "gen_vs_reco_pt_1j", 10, ptbins, 10, ptbins}; + genReco_pT_1j->GetYaxis()->SetTitle("p_{T} [GeV]"); + genReco_pT_1j->GetXaxis()->SetTitle("gen p_{T} [GeV]"); + + TH2F *genReco_pT_2j = new TH2F{"gen_vs_reco_pt_2j", "gen_vs_reco_pt_2j", 10, ptbins, 10, ptbins}; + genReco_pT_2j->GetYaxis()->SetTitle("p_{T} [GeV]"); + genReco_pT_2j->GetXaxis()->SetTitle("gen p_{T} [GeV]"); + + TH2F *genReco_pT_3pj = new TH2F{"gen_vs_reco_pt_3pj", "gen_vs_reco_pt_3pj", 10, ptbins, 10, ptbins}; + genReco_pT_3pj->GetYaxis()->SetTitle("p_{T} [GeV]"); + genReco_pT_3pj->GetXaxis()->SetTitle("gen p_{T} [GeV]"); + + TH2F *genReco_pT_inclJets = new TH2F{"gen_vs_reco_pt_inclJets", "gen_vs_reco_pt_inclJets", 10, ptbins, 10, ptbins}; + genReco_pT_inclJets->GetYaxis()->SetTitle("p_{T} [GeV]"); + genReco_pT_inclJets->GetXaxis()->SetTitle("gen p_{T} [GeV]"); + + int entries = Events->GetEntries(); + for (unsigned int i = 0; i < Events->GetEntries(); i ++) + { + Events->GetEntry(i); + if (i%100000 == 0) + { + std::cout << "Processing entry # " << i << " : " << ((float)i+1)*100/entries << " %\n"; + } + // Preselection of the HWW analysis, change it if needed + if (nLepton < 2 || Lepton_pt[0] < 25 || Lepton_pt[1] < 13 || Lepton_pt[2] > 10 || abs(Lepton_eta[0]) > 2.5 || abs(Lepton_eta[1]) > 2.5 || + mll < 60 || mll > 120 || Lepton_pdgId[0] * Lepton_pdgId[1] != -11 * 13) + continue; + + ptll_pull->Fill((ptll - gen_ptll)/gen_ptll, XSWeight); + genReco_pT_inclJets->Fill(gen_ptll, ptll, XSWeight); + if (nCleanJet == 0) + {genReco_pT_0j->Fill(gen_ptll, ptll, XSWeight);} + else if (nCleanJet == 1) + {genReco_pT_1j->Fill(gen_ptll, ptll, XSWeight);} + else if (nCleanJet == 2) + {genReco_pT_2j->Fill(gen_ptll, ptll, XSWeight);} + else if (nCleanJet >= 3) + {genReco_pT_3pj->Fill(gen_ptll, ptll, XSWeight);} + } + ptll_pull->Write(); + genReco_pT_inclJets->Write(); + genReco_pT_0j->Write(); + genReco_pT_1j->Write(); + genReco_pT_2j->Write(); + genReco_pT_3pj->Write(); + + auto normalize_columns = [](TH2F *h) { + int nx = h->GetNbinsX(); + int ny = h->GetNbinsY(); + for (int ix = 1; ix <= nx; ++ix) { // ROOT bins start at 1 + double col_sum = 0.0; + for (int iy = 1; iy <= ny; ++iy) { + col_sum += h->GetBinContent(ix, iy); + } + if (col_sum > 0) { + for (int iy = 1; iy <= ny; ++iy) { + double val = h->GetBinContent(ix, iy); + h->SetBinContent(ix, iy, val / col_sum); + } + } + } + }; + + auto draw_and_save = [&](TH2F *h, const char *tag) { + normalize_columns(h); + // gStyle->SetOptStat(0); + TCanvas *c = new TCanvas(Form("c_%s",tag), "",800,600); + c->cd(); + h->Draw("COLZ"); + gPad->Update(); // Ensure stat box is created + TPaveStats *st = (TPaveStats*)h->GetListOfFunctions()->FindObject("stats"); + if (st) { + std::cout<<" Stat box found! "<SetX1NDC(0.15); // left + st->SetX2NDC(0.45); // right + st->SetY1NDC(0.7); // top + st->SetY2NDC(0.9); // bottom + st->Draw(); + } + gPad->Update(); // Ensure stat box is created + TString cname = Form("genReco_pT2Dhist_%s_%d_%s_%s_%s.png", + tag, year, process.c_str(), algo.c_str(), WP.c_str()); + c->SaveAs(cname); + delete c; + }; + + TCanvas *c1 = new TCanvas("c1", "",800,600); + c1->cd(); + ptll_pull->Draw(); + ptll_pull->Fit("gaus"); // Fit with Gaussian + TString cname1 = "ptll_pull_" + std::to_string(year) + "_" + process + "_" + algo + "_" + WP + ".png"; + c1->SaveAs(cname1); + delete c1; + + draw_and_save(genReco_pT_inclJets, "inclJets"); + draw_and_save(genReco_pT_0j, "0j"); + draw_and_save(genReco_pT_1j, "1j"); + draw_and_save(genReco_pT_2j, "2j"); + draw_and_save(genReco_pT_3pj,"3pj"); + + +} diff --git a/ZpTreweighting/plot.py b/ZpTreweighting/plot.py index 9fa555cd..b1877b01 100644 --- a/ZpTreweighting/plot.py +++ b/ZpTreweighting/plot.py @@ -1,4 +1,7 @@ # Group plot +# Groups of samples to improve the plots. +# If not defined, normal plots is used + groupPlot = {} groupPlot['DY'] = { @@ -8,8 +11,17 @@ 'samples' : ['DY'] } +groupPlot['background'] = { + 'nameHR' : 'background', + 'isSignal' : 0, + 'color' : 851, # kAzure -9 + 'samples' : ['top', 'diboson', 'SMhiggs'] +} + # Plot +# keys here must match keys in samples.py + plot = {} plot['DY'] = { @@ -19,6 +31,26 @@ 'scale' : 1.0, } +plot['top'] = { + 'color' : 400, # kYellow + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + +plot['diboson'] = { + 'color' : 851, # kAzure -9 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + +plot['SMhiggs'] = { + 'color' : 632+3, # kRed+3 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} # Data plot['DATA'] = { diff --git a/ZpTreweighting/readme.md b/ZpTreweighting/readme.md new file mode 100644 index 00000000..0168e9f9 --- /dev/null +++ b/ZpTreweighting/readme.md @@ -0,0 +1,6 @@ +In order to produce the 2D histograms looking at gen_pTll vs pTll, run the following from the ZpTreweighting/ folder: +''' +root +.x genptll_vs_ptll.cc(2022, "DY", "DeepFlavB", "loose") +''' +The arguments (2022, "DY", "DeepFlavB", "loose") are the default ones, so those could be omitted. \ No newline at end of file diff --git a/ZpTreweighting/samples.py b/ZpTreweighting/samples.py index 4216032c..7d0c532d 100644 --- a/ZpTreweighting/samples.py +++ b/ZpTreweighting/samples.py @@ -4,21 +4,29 @@ redirector = "" useXROOTD = False +dataset_samples = 'amassiro' # MC: /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight # DATA: /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight -mcProduction = 'Summer22_130x_nAODv12_Full2022v12' -mcSteps = 'MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight' -dataReco = 'Run2022_ReReco_nAODv12_Full2022v12' -dataSteps = 'DATAl2loose2022v12__l2tight' +if dataset_samples == 'calderon': + mcProduction = 'Summer22_130x_nAODv12_Full2022v12' + mcSteps = 'MCl2loose2022v12__MCCorr2022v12JetScaling__sblancof__l2tight' # Using DYJetsToLL_M-50-LO from Calderon (DS, 22Nov25) + dataReco = 'Run2022_ReReco_nAODv12_Full2022v12' + dataSteps = 'DATAl2loose2022v12__sblancof__l2loose' +elif dataset_samples == 'amassiro': + mcProduction = 'Summer22_130x_nAODv12_Full2022v12_OLD' + mcSteps = 'MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight' # Using DYto2L-2Jets_MLL-50 from Amassiro (DS, 21Nov25) + dataReco = 'Run2022_ReReco_nAODv12_Full2022v12_OLD' + dataSteps = 'DATAl2loose2022v12__l2tight' + # fakeSteps = 'DATAl1loose2022EFGv12__fakeW' ############################################## ###### Tree base directory for the site ###### ############################################## -treeBaseDir = '/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano' -limitFiles = -1 +treeBaseDir = f'/eos/cms/store/group/phys_higgs/cmshww/{dataset_samples}/HWWNano' +limitFiles = -1 # For running on smaller set of samples (DS, 21Nov25) def makeMCDirectory(var=""): _treeBaseDir = treeBaseDir + "" @@ -98,7 +106,7 @@ def addSampleWeight(samples, sampleName, sampleNameType, weight): DataSets = ['MuonEG','SingleMuon','Muon','EGamma'] -# Proceeding with no HLT selection at the moment (DS, 19Nov25) +# Putting for later: HLT selections (DS, 19Nov25) DataTrig = { 'MuonEG' : ' Trigger_ElMu' , 'SingleMuon' : '!Trigger_ElMu && Trigger_sngMu' , @@ -122,7 +130,11 @@ def addSampleWeight(samples, sampleName, sampleNameType, weight): ########################################### # DY -files = nanoGetSampleFiles(mcDirectory, 'DYto2L-2Jets_MLL-50') +if dataset_samples == 'calderon': + files = nanoGetSampleFiles(mcDirectory, 'DYJetsToLL_M-50-LO') +elif dataset_samples == 'amassiro': + files = nanoGetSampleFiles(mcDirectory, 'DYto2L-2Jets_MLL-50') + samples['DY'] = { 'name': files, @@ -130,6 +142,48 @@ def addSampleWeight(samples, sampleName, sampleNameType, weight): 'FilesPerJob': 2, } +# remove backgrounds from data for ZpT reweighting: +top_samples = ['TTTo2L2Nu', 'TWminusto2L2Nu', 'TbarWplusto2L2Nu']#, 'ST_tW_top'] +diboson_samples = ['WWTo2L2Nu', 'WZTo3LNu', 'GluGlutoContintoWWtoENuENu', 'GluGlutoContintoWWtoENuMuNu', 'GluGlutoContintoWWtoENuTauNu', 'GluGlutoContintoWWtoMuNuENu', 'GluGlutoContintoWWtoMuNuMuNu', 'GluGlutoContintoWWtoMuNuTauNu', 'GluGlutoContintoWWtoTauNuENu', 'GluGlutoContintoWWtoTauNuMuNu', 'GluGlutoContintoWWtoTauNuTauNu', 'WGtoLNuG-1J_PTG10to100', 'WGtoLNuG-1J_PTG100to200', 'WGtoLNuG-1J_PTG200to400', 'WGtoLNuG-1J_PTG400to600', 'WGtoLNuG-1J_PTG600'] +higgs_samples = ['GluGluHToWWTo2L2Nu_M125', 'VBFHToWWTo2L2Nu_M125'] + +samples['top'] = { + 'name': nanoGetSampleFiles(mcDirectory, 'TTTo2L2Nu') + \ + nanoGetSampleFiles(mcDirectory, 'TWminusto2L2Nu') + \ + nanoGetSampleFiles(mcDirectory, 'TbarWplusto2L2Nu'), + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + +samples['diboson'] = { + 'name': nanoGetSampleFiles(mcDirectory, 'WWTo2L2Nu') + \ + nanoGetSampleFiles(mcDirectory, 'WZTo3LNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoENuENu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoENuMuNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoENuTauNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoMuNuENu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoMuNuMuNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoMuNuTauNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoTauNuENu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoTauNuMuNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoTauNuTauNu') + \ + nanoGetSampleFiles(mcDirectory, 'WGtoLNuG-1J_PTG10to100') + \ + nanoGetSampleFiles(mcDirectory, 'WGtoLNuG-1J_PTG100to200') + \ + nanoGetSampleFiles(mcDirectory, 'WGtoLNuG-1J_PTG200to400') + \ + nanoGetSampleFiles(mcDirectory, 'WGtoLNuG-1J_PTG400to600') + \ + nanoGetSampleFiles(mcDirectory, 'WGtoLNuG-1J_PTG600'), + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + +samples['SMhiggs'] = { + 'name': nanoGetSampleFiles(mcDirectory, 'GluGluHToWWTo2L2Nu_M125') + \ + nanoGetSampleFiles(mcDirectory, 'VBFHToWWTo2L2Nu_M125'), + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + + ########################################### ################## DATA ################### ########################################### diff --git a/ZpTreweighting/structure.py b/ZpTreweighting/structure.py index 508334d3..4c0f5428 100644 --- a/ZpTreweighting/structure.py +++ b/ZpTreweighting/structure.py @@ -8,6 +8,21 @@ 'isData' : 0 } +structure['top'] = { + 'isSignal' : 0, + 'isData' : 0 +} + +structure['diboson'] = { + 'isSignal' : 0, + 'isData' : 0 +} + +structure['SMhiggs'] = { + 'isSignal' : 0, + 'isData' : 0 +} + # data structure['DATA'] = { 'isSignal' : 0, diff --git a/ZpTreweighting/variables.py b/ZpTreweighting/variables.py index 27426716..94b55bfd 100644 --- a/ZpTreweighting/variables.py +++ b/ZpTreweighting/variables.py @@ -17,7 +17,7 @@ variables['mll'] = { 'name': 'mll', - 'range' : (50,0,200), + 'range' : (50,50,150), 'xaxis' : 'm_{ll} [GeV]', 'fold' : 0 } @@ -29,6 +29,21 @@ 'fold' : 0 } +# Data samples do not have gen-level information +# variables['gen_ptll'] = { +# 'name': 'gen_ptll', +# 'range' : (40,0,200), +# 'xaxis' : 'gen p_{T}^{ll} [GeV]', +# 'fold' : 0 +# } + +# variables['gen_Zpt'] = { +# 'name': 'gen_Zpt(nGenPart, GenPart_pt, GenPart_pdgId, GenPart_genPartIdxMother, GenPart_statusFlags, gen_ptll)', +# 'range' : (40,0,200), +# 'xaxis' : 'Gen p_{T}^{Z} [GeV]', +# 'fold' : 0 +# } + variables['drll'] = { 'name': 'drll', 'range' : (50, 0,5), From 82a3e3f5a0b1cc98afde518fa4864aab6f9f5759 Mon Sep 17 00:00:00 2001 From: CodexForster Date: Fri, 19 Dec 2025 08:13:00 -0600 Subject: [PATCH 06/13] Skeleton pipeline for BDT training in zh3l analysis --- ZH3l_BDT/readme.md | 6 +- ZH3l_BDT/zh3l_run2_bdt_rdf/README.md | 13 + ZH3l_BDT/zh3l_run2_bdt_rdf/aliases.py | 256 ++++++++ .../bdt_train/Classification_BDT.py | 75 +++ .../zh3l_run2_bdt_rdf/bdt_train/config_BDT.py | 74 +++ .../bdt_train/configuration_BDT.py | 17 + .../zh3l_run2_bdt_rdf/bdt_train/cuts_BDT.py | 144 +++++ .../bdt_train/preselections.py | 21 + .../bdt_train/samples_BDT.py | 417 +++++++++++++ .../zh3l_run2_bdt_rdf/bdt_train/structure.py | 119 ++++ ZH3l_BDT/zh3l_run2_bdt_rdf/configuration.py | 92 +++ ZH3l_BDT/zh3l_run2_bdt_rdf/cuts.py | 139 +++++ ZH3l_BDT/zh3l_run2_bdt_rdf/macros/ch_lll.cc | 41 ++ .../zh3l_run2_bdt_rdf/macros/flag_ossf.cc | 57 ++ ZH3l_BDT/zh3l_run2_bdt_rdf/macros/mOS_ll.cc | 54 ++ ZH3l_BDT/zh3l_run2_bdt_rdf/macros/m_lll.cc | 49 ++ ZH3l_BDT/zh3l_run2_bdt_rdf/macros/z_veto.cc | 56 ++ ZH3l_BDT/zh3l_run2_bdt_rdf/ngenjet.cc | 121 ++++ ZH3l_BDT/zh3l_run2_bdt_rdf/nuisances.py | 580 ++++++++++++++++++ ZH3l_BDT/zh3l_run2_bdt_rdf/plot.py | 249 ++++++++ ZH3l_BDT/zh3l_run2_bdt_rdf/samples.py | 416 +++++++++++++ ZH3l_BDT/zh3l_run2_bdt_rdf/structure.py | 113 ++++ .../zh3l_run2_bdt_rdf/valid_data_samples.txt | 1 + ZH3l_BDT/zh3l_run2_bdt_rdf/variables.py | 129 ++++ 24 files changed, 3236 insertions(+), 3 deletions(-) create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/README.md create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/aliases.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/Classification_BDT.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/config_BDT.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/configuration_BDT.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/cuts_BDT.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/preselections.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/samples_BDT.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/structure.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/configuration.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/cuts.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/macros/ch_lll.cc create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/macros/flag_ossf.cc create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/macros/mOS_ll.cc create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/macros/m_lll.cc create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/macros/z_veto.cc create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/ngenjet.cc create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/nuisances.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/plot.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/samples.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/structure.py create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/valid_data_samples.txt create mode 100644 ZH3l_BDT/zh3l_run2_bdt_rdf/variables.py diff --git a/ZH3l_BDT/readme.md b/ZH3l_BDT/readme.md index d4f7a5ab..cd139cb5 100644 --- a/ZH3l_BDT/readme.md +++ b/ZH3l_BDT/readme.md @@ -1,7 +1,7 @@ Building towards a set of codes for the ZH3l analysis using the PlotsConfigurationsRun3/RDF framework. Among the many changes we desire to implement, the following are the ones we're starting out with: -1. Make the Run2 codes of ZH3l analysis compatible with the Run3 framework. (Relevant folder: zh3l_run2_rdf) -2. Write BDT codes in Run3 framework for Run2 analysis/data. (Relevant folder: zh3l_run2_bdt_rdf) -3. Setup a flow enabling a classification neural network in PlotsConfigurationsRun3 / mkShapesRDF +1. Make the Run2 codes of ZH3l analysis compatible with the Run3 framework (Relevant folder: zh3l_run2_rdf). +2. Train a sample BDT in Run3 framework using Run2 data, weights, and scale-factors (Relevant folder: zh3l_run2_bdt_rdf). +3. Setup a full working pipeline enabling a classification neural network in PlotsConfigurationsRun3 / mkShapesRDF for the ZH3l analysis. Notes related to point 1 of the above list: Most of the changes include switching to RDF compliant expressions, commands and functions. diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/README.md b/ZH3l_BDT/zh3l_run2_bdt_rdf/README.md new file mode 100644 index 00000000..08d946c5 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/README.md @@ -0,0 +1,13 @@ +# Configuration for ZH3l BDT training + +The configurations and files to train BDT(s) for the ZH3l analysis are located in './bdt_train/'. The instructions to run the trainings follow. + python Classification_BDT.py +Currently no parameter/hyper-parameter optimization has been done. The model architecture and input parameters are also not designed/optimized for best performance. The codes simply establish the pipeline required to train and evaluate a BDT in the mkShapesRDF/PlotsConfigurationsRun3 framework. + +### Plot training results + +TBA + +# Evaluating trained BDT + +Work in progress. The configurations and files to run the analysis using the trained BDT are stored in './'. \ No newline at end of file diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/aliases.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/aliases.py new file mode 100644 index 00000000..0aeb3148 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/aliases.py @@ -0,0 +1,256 @@ +#Aliases (mostly btag) +import os +import copy +import inspect + +mc = [skey for skey in samples if skey not in ('Fake', 'DATA')] + +#2018 +#bWP = '0.1241' #Loose +bWP = '0.4184' +# DS - ideally from https://btv-wiki.docs.cern.ch/ScaleFactors/Run2UL2018/#general-remarks, but seems like this is calculated from another dataset: https://indico.cern.ch/event/826738/contributions/3464989/attachments/1860911/3058344/BTag_190612_PtRel.pdf. +configurations = os.path.realpath(inspect.getfile(inspect.currentframe())) # this file +configurations = os.path.dirname(configurations) # 2022_v12 +configurations = os.path.dirname(configurations) # WZ +configurations = os.path.dirname(configurations) # ControlRegions +configurations = os.path.dirname(configurations) # PlotsConfigurationsRun3 + +aliases = {} +aliases = OrderedDict() + +aliases['bVeto'] = { + 'expr': '(Sum((CleanJet_pt > 20.0 && abs(CleanJet_eta) < 2.5) * Take(Jet_btagDeepB, CleanJet_jetIdx) > ' + bWP + ') == 0)' + +} + +aliases['btagSF'] = { +'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>20 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_deepcsv_shape, CleanJet_jetIdx)+1*(CleanJet_pt<20 || abs(CleanJet_eta)>2.5))))', +'samples': mc +} + +systs = ['jes','lf','hf','lfstats1','lfstats2','hfstats1','hfstats2','cferr1','cferr2'] +# DS -(?)- jes - Jet Energy Scale uncertainty, lf/hf - light/heavy flavor, stats - statistical uncertainty, cferr - charm flavour + +for s in systs: + aliases['btagSF'+s+'up'] = { + 'expr': aliases['btagSF']['expr'].replace('shape','shape_up_'+s), + 'samples':mc + } + aliases['btagSF'+s+'down'] = { + 'expr': aliases['btagSF']['expr'].replace('shape','shape_down_'+s), + 'samples':mc + } + +aliases['Jet_PUIDSF'] = { + 'expr': 'TMath::Exp(Sum((Jet_jetId >= 2) * LogVec(Jet_PUIDSF_loose)))', + 'samples': mc +} + +aliases['Jet_PUIDSF_up'] = { + 'expr': 'TMath::Exp(Sum((Jet_jetId >= 2) * LogVec(Jet_PUIDSF_loose_up)))', + 'samples': mc +} + +aliases['Jet_PUIDSF_down'] = { + 'expr': 'TMath::Exp(Sum((Jet_jetId >= 2) * LogVec(Jet_PUIDSF_loose_down)))', + 'samples': mc +} + +aliases['PromptGenLepMatch3l'] = { + 'expr': 'Alt(Lepton_promptgenmatched, 0, 0) * Alt(Lepton_promptgenmatched, 1, 0) * Alt(Lepton_promptgenmatched, 2, 0)', + 'samples': mc +} + +#aliases['LepWPCutNew'] = { 'expr': '(((abs(Lepton_pdgId[0])==13 && Muon_mvaTTH[Lepton_muonIdx[0]]>0.8) || (abs(Lepton_pdgId[0])==11 && Electron_mvaTTH[Lepton_electronIdx[0]]>0.7)) && ((abs(Lepton_pdgId[1])==13 && Muon_mvaTTH[Lepton_muonIdx[1]]>0.8) || (abs(Lepton_pdgId[1])==11 && Electron_mvaTTH[Lepton_electronIdx[1]]>0.7)) && ((abs(Lepton_pdgId[2])==13 && Muon_mvaTTH[Lepton_muonIdx[2]]>0.8) || (abs(Lepton_pdgId[2])==11 && Electron_mvaTTH[Lepton_electronIdx[2]]>0.7)))', + # 'samples': mc + ['DATA'] +#} + +aliases['Top_pTrw'] = { + 'expr': '(topGenPt * antitopGenPt > 0.) * (TMath::Sqrt(TMath::Exp(0.0615 - 0.0005 * topGenPt) * TMath::Exp(0.0615 - 0.0005 * antitopGenPt))) + (topGenPt * antitopGenPt <= 0.)', + 'samples': ['top'] +} + +#aliases['ZH3l_dphilmetjj_test'] = { +# 'linesToAdd': [ +# '.L %s/src/PlotsConfigurations/Configurations/ZH3l/scripts/ZH3l_patch.cc+' % os.getenv('CMSSW_BASE') +# ], +# 'class': 'ZH3l_patch', +# 'args': ("dphilmetjj") +#} + +#aliases['ZH3l_dphilmetj_test'] = { +# 'class': 'ZH3l_patch', +# 'args': ("dphilmetj") +#} + +#aliases['ZH3l_mTlmet_test'] = { +# 'class': 'ZH3l_patch', +# 'args': ("mTlmet") +#} + +#aliases['ZH3l_mTlmetj_test'] = { +# 'class': 'ZH3l_patch', +# 'args': ("mTlmetj") +#} + +#aliases['ZH3l_mTlmetjj_test'] = { +# 'class': 'ZH3l_patch', +# 'args': ("mTlmetjj") +#} + +####################### +### SFs for tthMVA ### +####################### + +aliases['SFweightEleUp'] = { + 'expr': 'LepSF3l__ele_'+eleWP_new+'__Up', + 'samples': mc +} + +aliases['SFweightEleDown'] = { + 'expr': 'LepSF3l__ele_'+eleWP_new+'__Do', + 'samples': mc +} + +aliases['SFweightMuUp'] = { + 'expr': 'LepSF3l__mu_'+muWP_new+'__Up', + 'samples': mc +} + +aliases['SFweightMuDown'] = { + 'expr': 'LepSF3l__mu_'+muWP_new+'__Do', + 'samples': mc +} + + +#aliases['ttHMVA_SF_3l'] = { +# 'linesToAdd': ['.L %s/src/PlotsConfigurations/Configurations/patches/compute_SF_BETA.C+' % os.getenv('CMSSW_BASE')], +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'total_SF'), +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Up_0'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_up', 0), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Up_1'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_up', 1), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Up_2'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_up', 2), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Down_0'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_down', 0), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Down_1'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_down', 1), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_SF_Down_2'] = { +# 'class': 'compute_SF', +# 'args' : ('2018', 3, 'single_SF_down', 2), +# 'nominalOnly' : True, +# 'samples': mc +#} + +#aliases['ttHMVA_3l_ele_SF_Up'] = { +# 'expr' : '(ttHMVA_SF_Up_0[0]*(abs(Lepton_pdgId[0]) == 11) + (abs(Lepton_pdgId[0]) == 13)) *\ +# (ttHMVA_SF_Up_1[0]*(abs(Lepton_pdgId[1]) == 11) + (abs(Lepton_pdgId[1]) == 13)) *\ +# (ttHMVA_SF_Up_2[0]*(abs(Lepton_pdgId[2]) == 11) + (abs(Lepton_pdgId[2]) == 13))', +# 'nominalOnly' : True, +# 'samples' : mc +#} + +#aliases['ttHMVA_3l_ele_SF_Down'] = { +# 'expr' : '(ttHMVA_SF_Down_0[0]*(abs(Lepton_pdgId[0]) == 11) + (abs(Lepton_pdgId[0]) == 13)) *\ +# (ttHMVA_SF_Down_1[0]*(abs(Lepton_pdgId[1]) == 11) + (abs(Lepton_pdgId[1]) == 13)) *\ +# (ttHMVA_SF_Down_2[0]*(abs(Lepton_pdgId[2]) == 11) + (abs(Lepton_pdgId[2]) == 13))', +# 'nominalOnly' : True, +# 'samples' : mc +#} + +#aliases['ttHMVA_3l_mu_SF_Up'] = { +# 'expr' : '(ttHMVA_SF_Up_0[0]*(abs(Lepton_pdgId[0]) == 13) + (abs(Lepton_pdgId[0]) == 11)) *\ +# (ttHMVA_SF_Up_1[0]*(abs(Lepton_pdgId[1]) == 13) + (abs(Lepton_pdgId[1]) == 11)) *\ +# (ttHMVA_SF_Up_2[0]*(abs(Lepton_pdgId[2]) == 13) + (abs(Lepton_pdgId[2]) == 11))', +# 'nominalOnly' : True, +# 'samples' : mc +#} + +#aliases['ttHMVA_3l_mu_SF_Down'] = { +# 'expr' : '(ttHMVA_SF_Down_0[0]*(abs(Lepton_pdgId[0]) == 13) + (abs(Lepton_pdgId[0]) == 11)) *\ +# (ttHMVA_SF_Down_1[0]*(abs(Lepton_pdgId[1]) == 13) + (abs(Lepton_pdgId[1]) == 11)) *\ +# (ttHMVA_SF_Down_2[0]*(abs(Lepton_pdgId[2]) == 13) + (abs(Lepton_pdgId[2]) == 11))', +# 'nominalOnly' : True, +# 'samples' : mc +#} + +# In WpWmJJ_EWK events, partons [0] and [1] are always the decay products of the first W +aliases['lhe_mW1'] = { + 'expr': 'TMath::Sqrt(2. * Take(LHEPart_pt, 0) * Take(LHEPart_pt, 1) * (TMath::CosH(Take(LHEPart_eta, 0) - Take(LHEPart_eta, 1)) - TMath::Cos(Take(LHEPart_phi, 0) - Take(LHEPart_phi, 1))))', + 'samples': ['WWewk'] +} + +# and [2] [3] are the second W +aliases['lhe_mW2'] = { + 'expr': 'TMath::Sqrt(2. * Take(LHEPart_pt, 2) * Take(LHEPart_pt, 3) * (TMath::CosH(Take(LHEPart_eta, 2) - Take(LHEPart_eta, 3)) - TMath::Cos(Take(LHEPart_phi, 2) - Take(LHEPart_phi, 3))))', + 'samples': ['WWewk'] +} + +aliases['gstarHigh'] = { + 'expr': 'Gen_ZGstar_mass <0 || Gen_ZGstar_mass > 4', + 'samples': ['WZ'] +} + +# aliases['nCleanGenJet'] = { +# 'linesToAdd': ['.L %s/src/PlotsConfigurations/Configurations/Differential/ngenjet.cc+' % os.getenv('CMSSW_BASE') +# ], +# 'class': 'CountGenJet', +# 'samples': mc +# } + +# configurations = os.path.abspath('.') + '/' +# print(configurations) +# print('\n\n\n') + +# aliases['nCleanGenJet'] = { +# 'linesToAdd': ['.L %sngenjet.cc+' % configurations], +# 'class': 'CountGenJet', +# 'args': 'nLeptonGen, LeptonGen_isPrompt,\ +# LeptonGen_pdgId, LeptonGen_pt, LeptonGen_eta, LeptonGen_phi, \ +# LeptonGen_mass, nPhotonGen, PhotonGen_pt, PhotonGen_eta,PhotonGen_phi, \ +# PhotonGen_mass, nGenJet, GenJet_pt, GenJet_eta, GenJet_phi', +# 'samples': mc +# } + +#puidSFSource = '%s/src/LatinoAnalysis/NanoGardener/python/data/JetPUID_effcyandSF.root' % os.getenv('CMSSW_BASE') +#puidSFSource = '%s/src/PlotsConfigurations/Configurations/patches/PUID_80XTraining_EffSFandUncties.root' % os.getenv('CMSSW_BASE') + +#aliases['PUJetIdSF'] = { +# 'linesToAdd': [ +# 'gSystem->AddIncludePath("-I%s/src");' % os.getenv('CMSSW_BASE'), +# '.L %s/src/PlotsConfigurations/Configurations/patches/pujetidsf_event_new.cc+' % os.getenv('CMSSW_BASE') +# ], +# 'class': 'PUJetIdEventSF', +# 'args': (puidSFSource, '2018', 'loose'), +# 'samples': mc +#} diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/Classification_BDT.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/Classification_BDT.py new file mode 100644 index 00000000..bfe2a09e --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/Classification_BDT.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python +from ROOT import TMVA, TFile, TTree, TCut, TChain +from subprocess import call +from os.path import isfile + +import sys + +import config_BDT as config + +# Setup TMVA +def runJob(output_and_dataset_name = ""): + TMVA.Tools.Instance() + TMVA.PyMethodBase.PyInitialize() + + output = TFile.Open('TMVA{}.root'.format(output_and_dataset_name), 'RECREATE') + factory = TMVA.Factory('TMVAClassification', output,'!V:!Silent:Color:DrawProgressBar:AnalysisType=Classification') + # factory = TMVA.Factory('TMVAClassification', output,'!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=Classification') + + dataloader = TMVA.DataLoader("dataset{}".format(output_and_dataset_name)) + + for br in config.mvaVariables: + dataloader.AddVariable(br) + + for sampleName, sample in config.samples.items(): + isData = config.structure[sampleName]['isData'] + if (isinstance(isData, int) and isData == 1) or (not isinstance(isData, int) and 'all' in isData): + continue + + sample['tree'] = TChain("Events") + print("Sample name: ", sampleName) + for name, *location_weights in sample['name']: + print("Sub-sample: ", name) + locations = location_weights[0] + # weights = location_weights[1] if len(location_weights) > 1 else None + for loc in locations: + print("file: ", loc) + sample['tree'].Add(loc) + + if config.structure[sampleName]['isSignal']==1: + dataloader.AddSignalTree(sample['tree'], 1.0) + else: + dataloader.AddBackgroundTree(sample['tree'], 1.0) + # output_dim += 1 + # Reference: https://root.cern.ch/download/doc/tmva/TMVAUsersGuide.pdf + # Train test dataset will contain less/equal events compared to signal and background trees. How these events are chosen is given by the next line. Event weights are given by Monte Carlo generators, and may turn out to be overall very small or large. To avoid artifacts due to this, TMVA can internally renormalise the signal and background training using NormMode. + dataloader.PrepareTrainingAndTestTree(TCut(config.cut),'SplitMode=Random:NormMode=NumEvents:!V') + # dataloader.PrepareTrainingAndTestTree(TCut(config.cut),'nTrain_Signal=100000:nTrain_Background=100000:SplitMode=Random:NormMode=NumEvents:!V')#SSSF + + factory.BookMethod(dataloader, TMVA.Types.kBDT, "BDTG4", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=2" ) + # factory.BookMethod(dataloader, TMVA.Types.kBDT, "BDTG4D3", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=3" ) + # factory.BookMethod(dataloader, TMVA.Types.kBDT, "BDTG4D4", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=4" ) + # factory.BookMethod(dataloader, TMVA.Types.kBDT, "BDTG4D5", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=5" ) + # factory.BookMethod(dataloader, TMVA.Types.kBDT, "BDTG4D6", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=6" ) + # factory.BookMethod(dataloader, TMVA.Types.kBDT, "BDTG4C3", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=300:MaxDepth=2" ) + # factory.BookMethod(dataloader, TMVA.Types.kBDT, "BDTG4SK01", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.01:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=2" ) + # factory.BookMethod(dataloader, TMVA.Types.kBDT, "BDTG4F07" , "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.7:nCuts=500:MaxDepth=2" ) + # factory.BookMethod(dataloader, TMVA.Types.kBDT, "BDTG4SK01F07", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.01:UseBaggedBoost:GradBaggingFraction=0.7:nCuts=500:MaxDepth=2" ) + # Run training, test and evaluation + factory.TrainAllMethods() + factory.TestAllMethods() + factory.EvaluateAllMethods() + + output.Close() + +if __name__ == "__main__": + + print("Input arguments: {}".format(sys.argv)) + if len(sys.argv) > 1: + print("Suffix is: {}".format(sys.argv[1])) + output_and_dataset = sys.argv[1] + runJob(output_and_dataset) + os.system("mv dataset dataset{}".format(output_and_dataset)) + else: + print("No suffix, running with standard output name") + runJob() diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/config_BDT.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/config_BDT.py new file mode 100644 index 00000000..bba9cc4c --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/config_BDT.py @@ -0,0 +1,74 @@ +#!/usr/bin/env python + +from __future__ import print_function +import os +from ROOT import gROOT, TFile, TChain, TCut + +# import models +import preselections + +isDEV=False + +# Load configuration +with open("configuration_BDT.py") as handle: + exec(handle.read()) # Read the file content as a string + +samples={} +structure={} +cuts={} +for f in [samplesFile, structureFile, cutsFile]: + with open(f) as handle: + exec(handle.read()) + + +# Reduce sample files for fast dev +if isDEV: + for sampleName, sample in samples.items(): + if sampleName not in ['DY', 'top', 'ttV', 'WW', 'Zg', 'ZgS', 'WZ', 'ZZ', 'VVV', 'ZH_hww','ggZH_hww','WH_hww','ttH_hww', 'ZH_htt', 'WH_htt', 'Fake_e', 'Fake_m']: + # if sampleName not in ['Wg','Zg','WgS','ZgS','ZZ','WZ','top','DY','WH_hww_plus','WH_hww_minus','WH_htt_plus','WH_htt_minus']: + samples.pop(sampleName) + continue + +# Define data to be loaded +with open("./preselections.py") as handle: + exec(handle.read()) + +cut="(({0}) && ({1}))".format(cuts['NONE'],preselections['ALL']) + +# mvaVariables = [ +# 'CleanJet_pt[0][0]', +# 'ZH3l_dphilmetjj', +# 'PuppiMET_pt', +# 'Lepton_pt[0][0]', +# 'Lepton_pt[1][0]', +# 'Lepton_pt[2][0]' +# ] + +mvaVariables = [ + 'Alt$( CleanJet_pt[0], 0)', + 'ZH3l_dphilmetjj', + 'PuppiMET_pt', + 'Alt$( Lepton_pt[0], 0)', + 'Alt$( Lepton_pt[1], 0)', + 'Alt$( Lepton_pt[2], 0)' +] + +# : 1 : WH3l_ZVeto : 8.013e-02 +# : 2 : MinIf$(WH3l_mOSll[],WH3l_mOSll[Iteration$]>0) : 6.387e-02 +# : 3 : MinIf$(WH3l_ptOSll[],WH3l_ptOSll[Iteration$]>0) : 6.168e-02 +# : 4 : WH3l_dphilmet[0] : 5.700e-02 +# : 5 : Alt$(Lepton_pt[0],0) : 5.583e-02 +# : 6 : WH3l_dphilmet[2] : 5.460e-02 +# : 7 : WH3l_ptlll : 5.448e-02 +# : 8 : WH3l_dphilllmet : 5.393e-02 +# : 9 : WH3l_mtWWW : 5.162e-02 +# : 10 : WH3l_mlll : 5.103e-02 +# : 11 : WH3l_ptWWW : 5.083e-02 +# : 12 : MinIf$(WH3l_drOSll[],WH3l_drOSll[Iteration$]>0) : 4.966e-02 +# : 13 : PuppiMET_pt : 4.907e-02 +# : 14 : WH3l_mtlmet[1] : 4.747e-02 +# : 15 : WH3l_dphilmet[1] : 4.527e-02 +# : 16 : Alt$(Lepton_pt[1],0) : 4.456e-02 +# : 17 : WH3l_mtlmet[2] : 4.388e-02 +# : 18 : WH3l_mtlmet[0] : 4.260e-02 +# : 19 : Alt$(Lepton_pt[2],0) : 4.250e-02 diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/configuration_BDT.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/configuration_BDT.py new file mode 100644 index 00000000..7dc6dfd2 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/configuration_BDT.py @@ -0,0 +1,17 @@ +# example of configuration file + +import os + +tagName = '' + +# luminosity to normalize to (in 1/fb) +lumi = 59.83 + +# file with list of cuts +cutsFile = 'cuts_BDT.py' + +# file with list of samples +samplesFile = 'samples_BDT.py' + +# structure file for datacard +structureFile = 'structure.py' diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/cuts_BDT.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/cuts_BDT.py new file mode 100644 index 00000000..7281a38a --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/cuts_BDT.py @@ -0,0 +1,144 @@ +# cuts + +cuts = {} +# 'MinIf$( WH3l_mOSll[], WH3l_mOSll[Iteration$] > 0) > 12 \ +# && Alt$(Lepton_pt[0],0)>25 \ +# && Alt$(Lepton_pt[1],0)>10 \ +# && Alt$(Lepton_pt[2],0)>10 \ +# && (nLepton>=3 && Alt$(Lepton_pt[3],0)<10) \ +# && abs(WH3l_chlll) == 1 \ +# && Alt$(CleanJet_pt[0], 0) < 30 \ +# && WH3l_flagOSSF == 1 \ +# && WH3l_ZVeto > 20 \ +# && PuppiMET_pt > 40 \ +# ' + +cuts['NONE'] = '1' # No cuts for BDT training + +cuts['zmass_cut'] = 'WH3l_ZVeto < 25' + +cuts['jet_cut_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + ' + +cuts['bveto_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + ' + +cuts['z4lveto_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + ' + +cuts['zh3l_SR_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetjj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetjj > 3.14159/2 \ + ' + +cuts['met_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + ' + +cuts['zh3l_SR_2j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetjj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_2j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetjj > 3.14159/2 \ + ' + +cuts['jet_cut_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + ' + +cuts['bveto_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + ' + +cuts['z4lveto_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + ' + +cuts['zh3l_SR_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetj > 3.14159/2 \ + ' + +cuts['met_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + ' + +cuts['zh3l_SR_1j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_1j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetj > 3.14159/2 \ + ' + +''' + #11 = e +# 13 = mu +# 15 = tau +''' diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/preselections.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/preselections.py new file mode 100644 index 00000000..5b66af1a --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/preselections.py @@ -0,0 +1,21 @@ +preselections = {} + +# preselections['ALL'] = 'Lepton_pt[0][0]>25 \ +# && Lepton_pt[1][0]>20 \ +# && Lepton_pt[2][0]>15 \ +# && Lepton_pt[3][0]<10 \ +# && (WH3l_mOSll[0] < 0 || WH3l_mOSll[0] > 12) \ +# && (WH3l_mOSll[1] < 0 || WH3l_mOSll[1] > 12) \ +# && (WH3l_mOSll[2] < 0 || WH3l_mOSll[2] > 12) \ +# && abs(WH3l_chlll) == 1 \ +# ' + +preselections['ALL'] = 'Alt$( Lepton_pt[0], 0) > 25 \ + && Alt$( Lepton_pt[1], 0) > 20 \ + && Alt$( Lepton_pt[2], 0) > 15 \ + && Alt$( Lepton_pt[3], 0) < 10 \ + && (WH3l_mOSll[0] < 0 || WH3l_mOSll[0] > 12) \ + && (WH3l_mOSll[1] < 0 || WH3l_mOSll[1] > 12) \ + && (WH3l_mOSll[2] < 0 || WH3l_mOSll[2] > 12) \ + && abs(WH3l_chlll) == 1 \ + ' \ No newline at end of file diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/samples_BDT.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/samples_BDT.py new file mode 100644 index 00000000..0d7178db --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/samples_BDT.py @@ -0,0 +1,417 @@ +import os +import subprocess + +# global getSampleFiles +# from LatinoAnalysis.Tools.commonTools import getSampleFiles, addSampleWeight, getBaseWnAOD +# It looks like getBaseWnAOD and addSampleWeight from Latinos does the same job as CombineBaseW function from makeShapesRDF. Similarly getSampleFiles function from Latinos is the same as nanoGetSampleFiles from makeShapesRDF. + +from mkShapesRDF.lib.search_files import SearchFiles +searchFiles = SearchFiles() +redirector = "" +limitFiles = 1 +samples = {} + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + return [(name, _files[:limitFiles])] + else: + return [(name, _files)] + +def getSampleFilesNano(inputDir,Sample,absPath=False): + # return getSampleFiles(inputDir,Sample,absPath,'nanoLatino_') + return nanoGetSampleFiles(inputDir, Sample) + +def CombineBaseW(samples, proc, samplelist): + _filtFiles = list(filter(lambda k: k[0] in samplelist, samples[proc]["name"])) + _files = list(map(lambda k: k[1], _filtFiles)) + _l = list(map(lambda k: len(k), _files)) + leastFiles = _files[_l.index(min(_l))] + dfSmall = ROOT.RDataFrame("Runs", leastFiles) + s = dfSmall.Sum("genEventSumw").GetValue() + f = ROOT.TFile(leastFiles[0]) + t = f.Get("Events") + t.GetEntry(1) + xs = t.baseW * s + + __files = [] + for f in _files: + __files += f + df = ROOT.RDataFrame("Runs", __files) + s = df.Sum("genEventSumw").GetValue() + newbaseW = str(xs / s) + return newbaseW # "/baseW is used after getBaseWnAOD/CombineBaseW calls in this code" + # weight = newbaseW + "/baseW" + + # for iSample in samplelist: + # addSampleWeight(samples, proc, iSample, weight) + + +def addSampleWeight(samples, sampleName, sampleNameType, weight): + obj = list(filter(lambda k: k[0] == sampleNameType, samples[sampleName]["name"]))[0] + samples[sampleName]["name"] = list( + filter(lambda k: k[0] != sampleNameType, samples[sampleName]["name"]) + ) + if len(obj) > 2: + samples[sampleName]["name"].append( + (obj[0], obj[1], obj[2] + "*(" + weight + ")") + ) + else: + samples[sampleName]["name"].append((obj[0], obj[1], "(" + weight + ")")) + + +############################################## +###### Tree Directory according to site ###### +############################################## + +SITE=os.uname()[1] +xrootdPath='' +if 'iihe' in SITE : + xrootdPath = 'dcap://maite.iihe.ac.be/' + treeBaseDir = '/pnfs/iihe/cms/store/user/xjanssen/HWW2015/' +elif 'cern' in SITE : + #xrootdPath='root://eoscms.cern.ch/' + treeBaseDir = '/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/' + +directory = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7' + +################################################ +############ NUMBER OF LEPTONS ################# +################################################ + +#Nlep='2' +Nlep='3' +#Nlep='4' + +################################################ +############### Lepton WP ###################### +################################################ + +eleWP='mvaFall17V1Iso_WP90' +#eleWP='mvaFall17V1Iso_WP90_SS' +#eleWP='mvaFall17V2Iso_WP90' +#eleWP='mvaFall17V2Iso_WP90_SS' +muWP ='cut_Tight_HWWW' +eleWP_new = 'mvaFall17V1Iso_WP90_tthmva_70' +muWP_new = 'cut_Tight_HWWW_tthmva_80' + +LepWPCut = 'LepCut'+Nlep+'l__ele_'+eleWP_new+'__mu_'+muWP_new +#LepWPCut = 'LepCut'+Nlep+'l__ele_'+eleWP+'__mu_'+muWP +#LepWPweight = 'ttHMVA_SF_3l[0]' #SF for new WPs, defined in aliases +LepWPweight = 'LepSF'+Nlep+'l__ele_'+eleWP_new+'__mu_'+muWP_new + +################################################ +############ BASIC MC WEIGHTS ################## +################################################ + +XSWeight = 'XSWeight' +SFweight = 'SFweight'+Nlep+'l*'+LepWPweight+'*'+LepWPCut+'*Jet_PUIDSF' +PromptGenLepMatch = 'PromptGenLepMatch'+Nlep+'l' + +################################################ +############## FAKE WEIGHTS #################### +################################################ + +#eleWP_new = 'mvaFall17V1Iso_WP90_tthmva_70' +#muWP_new = 'cut_Tight_HWWW_tthmva_80' + +if Nlep == '2' : + fakeW = 'fakeW2l_ele_'+eleWP_new+'_mu_'+muWP_new + #fakeW = 'fakeW2l_ele_'+eleWP+'_mu_'+muWP +else: + fakeW = 'fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_'+Nlep+'l' + #fakeW = 'fakeW_ele_'+eleWP+'_mu_'+muWP+'_'+Nlep+'l' + +################################################ +############### B-Tag WP ###################### +################################################ + +SFweight += '*btagSF' #define in aliases.py + +################################################ +############ MET FILTERS ################### +################################################ + +METFilter_MC = 'METFilter_MC' +METFilter_DATA = 'METFilter_DATA' + +################################################ +############ DATA DECLARATION ################## +################################################ + +DataRun = [ + ['A','Run2018A-02Apr2020-v1'] , + ['B','Run2018B-02Apr2020-v1'] , + ['C','Run2018C-02Apr2020-v1'] , + ['D','Run2018D-02Apr2020-v1'] , + ] + +DataSets = ['MuonEG','DoubleMuon','SingleMuon','EGamma'] + +DataTrig = { + 'MuonEG' : 'Trigger_ElMu' , + 'DoubleMuon' : '!Trigger_ElMu && Trigger_dblMu' , + 'SingleMuon' : '!Trigger_ElMu && !Trigger_dblMu && Trigger_sngMu' , + 'EGamma' : '!Trigger_ElMu && !Trigger_dblMu && !Trigger_sngMu && (Trigger_sngEl || Trigger_dblEl)' , + } + +########################################### +############# BACKGROUNDS ############### +########################################### + +############ DY ############ + +ptllDYW_NLO = '(0.87*(gen_ptll<10)+(0.379119+0.099744*gen_ptll-0.00487351*gen_ptll**2+9.19509e-05*gen_ptll**3-6.0212e-07*gen_ptll**4)*(gen_ptll>=10 && gen_ptll<45)+(9.12137e-01+1.11957e-04*gen_ptll-3.15325e-06*gen_ptll**2-4.29708e-09*gen_ptll**3+3.35791e-11*gen_ptll**4)*(gen_ptll>=45 && gen_ptll<200) + 1*(gen_ptll>200))' +ptllDYW_LO = '((0.632927+0.0456956*gen_ptll-0.00154485*gen_ptll*gen_ptll+2.64397e-05*gen_ptll*gen_ptll*gen_ptll-2.19374e-07*gen_ptll*gen_ptll*gen_ptll*gen_ptll+6.99751e-10*gen_ptll*gen_ptll*gen_ptll*gen_ptll*gen_ptll)*(gen_ptll>0)*(gen_ptll<100)+(1.41713-0.00165342*gen_ptll)*(gen_ptll>=100)*(gen_ptll<300)+1*(gen_ptll>=300))' +Zgfilter = '( !(Sum(PhotonGen_isPrompt==1 && PhotonGen_pt>15 && abs(PhotonGen_eta)<2.6) > 0 && Sum(LeptonGen_isPrompt==1 && LeptonGen_pt>15)>=2) )' #Zg sample uses photon pt > 15, lepton pt > 15 + +samples['DY'] = { 'name' : getSampleFilesNano(directory,'DYJetsToLL_M-10to50-LO') + + getSampleFilesNano(directory,'DYJetsToLL_M-10to50-LO_ext1') + + getSampleFilesNano(directory,'DYJetsToLL_M-50-LO') + + getSampleFilesNano(directory, 'DYJetsToLL_M-50') + + getSampleFilesNano(directory, 'DYJetsToLL_M-50_ext2') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-100to200') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-200to400') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-400to600') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-600toInf') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-70to100') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-100to200') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-200to400') + # + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-400to600') + # + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-400to600_ext2') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-600to800') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-800to1200') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-1200to2500') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-2500toInf'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC +'*'+Zgfilter, + 'FilesPerJob' : 5, + 'suppressNegative' :['all'], + 'suppressNegativeNuisances' :['all'], + } + +M10baseW = '1'#CombineBaseW(directory,'Autumn18_102X_nAODv7_Full2018v7',['DYJetsToLL_M-10to50-LO','DYJetsToLL_M-10to50-LO_ext1']) +#HT400baseW = getBaseWnAOD(directory,'Autumn18_102X_nAODv7_Full2018v7',['DYJetsToLL_M-50_HT-400to600']) + +addSampleWeight(samples,'DY','DYJetsToLL_M-10to50-LO' ,ptllDYW_LO+'*(LHE_HT<100.0)*'+M10baseW+'/baseW') +addSampleWeight(samples,'DY','DYJetsToLL_M-10to50-LO_ext1' ,ptllDYW_LO+'*(LHE_HT<100.0)*'+M10baseW+'/baseW') +addSampleWeight(samples,'DY','DYJetsToLL_M-50-LO' ,ptllDYW_LO+'*(LHE_HT<70.0)') +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-100to200' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-200to400' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-400to600' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-600toInf' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-70to100' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-100to200' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-200to400' ,ptllDYW_LO) +#addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-400to600' ,ptllDYW_LO+'*'+HT400baseW+'/baseW') +#addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-400to600_ext2',ptllDYW_LO+'*'+HT400baseW+'/baseW') +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-600to800' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-800to1200' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-1200to2500' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-2500toInf' ,ptllDYW_LO) +addSampleWeight(samples,'DY', 'DYJetsToLL_M-50' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_ext2' ,ptllDYW_LO) + +############ Top ############ + +samples['top'] = { 'name' : getSampleFilesNano(directory,'TTTo2L2Nu') + + getSampleFilesNano(directory,'ST_s-channel_ext1') + # + getSampleFilesNano(directory,'ST_t-channel_antitop') + + getSampleFilesNano(directory,'ST_t-channel_top') + + getSampleFilesNano(directory,'ST_tW_antitop_ext1') + + getSampleFilesNano(directory,'ST_tW_top_ext1'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + 'FilesPerJob' : 6, + } + +addSampleWeight(samples,'top','TTTo2L2Nu','Top_pTrw') + +samples['ttV'] = { 'name' : getSampleFilesNano(directory,'TTWJetsToLNu') + + getSampleFilesNano(directory,'TTZjets'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + 'FilesPerJob' : 5, + } + + +############ WW ############ + + +samples['WW'] = { 'name' : getSampleFilesNano(directory,'WWTo2L2Nu'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*nllW', + } + +#samples['WWewk'] = { 'name' : getSampleFilesNano(directory,'WpWmJJ_EWK_QCD_noTop_noHiggs') +# + getSampleFilesNano(directory,'WpWpJJ_EWK_QCD'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*(Sum$(abs(GenPart_pdgId)==6 || GenPart_pdgId==25)==0)*(lhe_mW1[0] > 60. && lhe_mW1[0] < 100. && lhe_mW2[0] > 60. && lhe_mW2[0] < 100.)', +# } + +#samples['ggWW'] = { 'name' : getSampleFilesNano(directory,'GluGluToWWToENEN') +# + getSampleFilesNano(directory,'GluGluToWWToENMN') +# + getSampleFilesNano(directory,'GluGluToWWToENTN') +# + getSampleFilesNano(directory,'GluGluToWWToMNEN') +# + getSampleFilesNano(directory,'GluGluToWWToMNMN') +# + getSampleFilesNano(directory,'GluGluToWWToMNTN') +# + getSampleFilesNano(directory,'GluGluToWWToTNEN') +# + getSampleFilesNano(directory,'GluGluToWWToTNMN') +# + getSampleFilesNano(directory,'GluGluToWWToTNTN'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*1.53/1.4', +# } + +###### Zg + +samples['Zg'] = { 'name' : getSampleFilesNano(directory,'ZGToLLG'), + 'weight' : XSWeight+'*'+SFweight+'*'+METFilter_MC + '*(Gen_ZGstar_mass <= 0)', + 'FilesPerJob' : 6 , + } + +###### Zg* + +samples['ZgS'] = { 'name' : getSampleFilesNano(directory,'ZGToLLG'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*(Gen_ZGstar_mass > 0)', + 'FilesPerJob' : 4 , + } + +##### WZ + +samples['WZ'] = { 'name': getSampleFilesNano(directory,'WZTo3LNu_mllmin01') + + getSampleFilesNano(directory,'WZTo2L2Q'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*(gstarHigh)' , + 'FilesPerJob' : 5 , + } + +##### ZZ + +samples['ZZ'] = { 'name' : getSampleFilesNano(directory,'ZZTo2L2Nu_ext1') + + getSampleFilesNano(directory,'ZZTo2L2Nu_ext2') + + getSampleFilesNano(directory,'ZZTo2L2Q') + + getSampleFilesNano(directory,'ZZTo4L_ext1') + + getSampleFilesNano(directory,'ZZTo4L_ext2') + #+ getSampleFilesNano(directory,'ggZZ4m') #Missing file for ElepTup + + getSampleFilesNano(directory,'ggZZ4m_ext1') + + getSampleFilesNano(directory,'ggZZ4t') + + getSampleFilesNano(directory,'ggZZ2e2t') + + getSampleFilesNano(directory,'ggZZ2m2t') + + getSampleFilesNano(directory,'ggZZ2e2m'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC, + 'FilesPerJob' : 3, + } + +ZZ2LbaseW = '1'#CombineBaseW(directory,'Autumn18_102X_nAODv7_Full2018v7',['ZZTo2L2Nu_ext1','ZZTo2L2Nu_ext2']) +ZZ4LbaseW = '1'#CombineBaseW(directory,'Autumn18_102X_nAODv7_Full2018v7',['ZZTo4L_ext1', 'ZZTo4L_ext2']) +#ggZZbaseW = getBaseWnAOD(directory,'Autumn18_102X_nAODv7_Full2018v7',['ggZZ4m', 'ggZZ4m_ext1']) + +addSampleWeight(samples,'ZZ','ZZTo2L2Nu_ext1',"1.07*"+ZZ2LbaseW+"/baseW") ## The non-ggZZ NNLO/NLO k-factor, cited from https://arxiv.org/abs/1405.2219v1 +addSampleWeight(samples,'ZZ','ZZTo2L2Nu_ext2',"1.07*"+ZZ2LbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ZZTo2L2Q', "1.07") +addSampleWeight(samples,'ZZ','ZZTo4L_ext1', "1.07*"+ZZ4LbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ZZTo4L_ext2', "1.07*"+ZZ4LbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ggZZ2e2t', "1.68") ## The NLO/LO k-factor, cited from https://arxiv.org/abs/1509.06734v1 +addSampleWeight(samples,'ZZ','ggZZ2m2t', "1.68") +addSampleWeight(samples,'ZZ','ggZZ2e2m', "1.68") +#addSampleWeight(samples,'ZZ','ggZZ4m', "1.68*"+ggZZbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ggZZ4m_ext1', "1.68") +addSampleWeight(samples,'ZZ','ggZZ4t', "1.68") + +############ VVV ############ + +samples['VVV'] = { 'name' : getSampleFilesNano(directory,'ZZZ') + + getSampleFilesNano(directory,'WZZ') + + getSampleFilesNano(directory,'WWZ') + + getSampleFilesNano(directory,'WWW'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +########################################## +################ SIGNALS ################# +########################################## + +############ ZH H->WW ############ + +samples['ZH_hww'] = { 'name' : getSampleFilesNano(directory,'HZJ_HToWW_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +samples['ggZH_hww'] = { 'name' : getSampleFilesNano(directory,'GluGluZH_HToWW_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +############ WH H->WW ############ + +samples['WH_hww'] = { 'name' : getSampleFilesNano(directory,'HWplusJ_HToWW_M125') + + getSampleFilesNano(directory,'HWminusJ_HToWW_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +############ ttH ############ + +samples['ttH_hww'] = { 'name' : getSampleFilesNano(directory,'ttHToNonbb_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +############ bbH ############ +# Not available for Latinos 2016 v6 + +############ H->TauTau ############ + +#samples['ggH_htt'] = { 'name' : getSampleFilesNano(directory,'GluGluHToTauTau_M125'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , +# } + +#samples['qqH_htt'] = { 'name' : getSampleFilesNano(directory,'VBFHToTauTau_M125'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , +# } + +samples['ZH_htt'] = { 'name' : getSampleFilesNano(directory,'HZJ_HToTauTau_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +samples['WH_htt'] = { 'name' : getSampleFilesNano(directory,'HWplusJ_HToTauTau_M125') + + getSampleFilesNano(directory,'HWminusJ_HToTauTau_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +########################################### +################## FAKE ################### +########################################### + +samples['Fake'] = { 'name': [ ] , + 'weight' : fakeW+'*'+METFilter_DATA, + 'weights' : [ ] , + 'isData': ['all'], + 'FilesPerJob' : 500 , + 'suppressNegativeNuisances' :['all'], + } + +directory = treeBaseDir+'Run2018_102X_nAODv7_Full2018v7/DATAl1loose2018v7__l2loose__fakeW/' +#directory = treeBaseDir+'Run2018_102X_nAODv6_Full2018v6/DATAl1loose2018v6__l2loose__fakeW/' +for Run in DataRun : + for DataSet in DataSets : + FileTarget = getSampleFilesNano(directory,DataSet+'_'+Run[1],True) + for iFile in FileTarget: + samples['Fake']['name'].append(iFile) + samples['Fake']['weights'].append(DataTrig[DataSet]) + +# Commented out by DS to help facilitate BDT training, 18Dec25 +# samples['Fake']['subsamples'] = { +# 'e': 'abs(ZH3l_pdgid_l) == 11', +# 'm': 'abs(ZH3l_pdgid_l) == 13' +# } + +########################################### +################## DATA ################### +########################################### + +samples['DATA'] = { 'name': [ ] , + 'weight' : METFilter_DATA+'*'+LepWPCut, + 'weights' : [ ], + 'isData': ['all'], + 'FilesPerJob' : 500 , + } + +directory = treeBaseDir+'/Run2018_102X_nAODv7_Full2018v7/DATAl1loose2018v7__l2loose__l2tightOR2018v7/' +for Run in DataRun : + for DataSet in DataSets : + FileTarget = getSampleFilesNano(directory,DataSet+'_'+Run[1],True) + for iFile in FileTarget: + samples['DATA']['name'].append(iFile) + samples['DATA']['weights'].append(DataTrig[DataSet]) + diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/structure.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/structure.py new file mode 100644 index 00000000..e6836144 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/bdt_train/structure.py @@ -0,0 +1,119 @@ +# structure configuration for datacard +# keys here must match keys in samples.py +structure = {} + +# Backgrounds + +# structure['Fake_e'] = { +# 'isSignal' : 0, +# 'isData' : 0, +# } + +# structure['Fake_m'] = { +# 'isSignal' : 0, +# 'isData' : 0, +# } +# Both fake subsamples merged into one for BDT training (DS, 18Dec25) +structure['Fake'] = { + 'isSignal' : 0, + 'isData' : 0, + } + +structure['DY'] = { + 'isSignal' : 0, + 'isData' : 0, + 'removeFromCuts' : ['zh3l_SR_1j'] + } + +structure['Zg'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['ZgS'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['WW'] = { + 'isSignal' : 0, + 'isData' : 0, + 'removeFromCuts' : ['zh3l_SR_1j','zh3l_SR_2j'] + } + +#structure['WWewk'] = { +# 'isSignal' : 0, +# 'isData' : 0 +# } + +#structure['ggWW'] = { +# 'isSignal' : 0, +# 'isData' : 0 +# } + +structure['WZ'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['VVV'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['ZZ'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['ttV'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['top'] = { + 'isSignal' : 0, + 'isData' : 0, + 'removeFromCuts' : ['zh3l_WZ_CR_1j','zh3l_SR_1j'] + } + +# Signal +structure['WH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ZH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ggZH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ttH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['WH_htt'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ZH_htt'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +# Data +structure['DATA'] = { + 'isSignal' : 0, + 'isData' : 1 + } + + + + diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/configuration.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/configuration.py new file mode 100644 index 00000000..51d4dfda --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/configuration.py @@ -0,0 +1,92 @@ +import sys,os + +# tag used to identify the configuration folder version +tag = 'ZH3l_BDTrun2' + +# file to use as runner script, default uses mkShapesRDF.shapeAnalysis.runner, otherwise specify path to script +runnerFile = "default" + +# output file name +outputFile = "mkShapes__{}.root".format(tag) + +# path to ouput folder +outputFolder = "/eos/user/" + os.getlogin()[0] + "/" + os.getlogin() + "/mkShapesRDF_rootfiles/" + tag + "/rootFile/" + +# path to batch folder (used for condor submission) +batchFolder = "condor" + +# path to configuration folder (will contain all the compiled configuration files) +configsFolder = "configs" + +# luminosity to normalize to (in 1/fb) +# https://github.com/latinos/mkShapesRDF/blob/Run3/mkShapesRDF/processor/data/TrigMaker_cfg.py#L1016 +lumi = 8.174732641 + +# file with list of variables +variablesFile = 'variables.py' + +# file with TTree aliases +aliasesFile = 'aliases.py' + +# file with list of cuts +cutsFile = 'cuts.py' + +# file with list of samples +samplesFile = 'samples.py' + +# file with list of samples +plotFile = 'plot.py' + +# structure file for datacard +structureFile = 'structure.py' + +# nuisances file for mkDatacards and for mkShape +nuisancesFile = 'nuisances.py' + +# path to folder where to save plots +plotPath = "plots_" + tag + +# this lines are executed right before the runner on the condor node +mountEOS = [ + # "export KRB5CCNAME=/home/gpizzati/krb5\n", +] + +# list of imports to import when compiling the whole configuration folder, it should not contain imports used by configuration.py +imports = ["os", "glob", ("collections", "OrderedDict"), "ROOT"] + +# list of files to compile +filesToExec = [ + samplesFile, + aliasesFile, + cutsFile, + variablesFile, + plotFile, + nuisancesFile, + structureFile, +] + +# list of variables to keep in the compiled configuration folder +varsToKeep = [ + "batchVars", + "outputFolder", + "batchFolder", + "configsFolder", + "outputFile", + "runnerFile", + "tag", + "samples", + "aliases", + "variables", + ("cuts", {"cuts": "cuts", "preselections": "preselections"}), + ("plot", {"plot": "plot", "groupPlot": "groupPlot", "legend": "legend"}), + "nuisances", + "structure", + "lumi", +] + +# list of variables to keep in the batch submission script (script.py) +batchVars = varsToKeep[varsToKeep.index("samples") :] + + +varsToKeep += ['plotPath'] + diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/cuts.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/cuts.py new file mode 100644 index 00000000..a620ab34 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/cuts.py @@ -0,0 +1,139 @@ +cuts = {} + +preselections = 'Alt( Lepton_pt, 0, 0)>25 \ + && Alt( Lepton_pt, 1, 0)>20 \ + && Alt( Lepton_pt, 2, 0)>15 \ + && Alt( Lepton_pt, 3, 0)<10 \ + && (WH3l_mOSll[0] < 0 || WH3l_mOSll[0] > 12) \ + && (WH3l_mOSll[1] < 0 || WH3l_mOSll[1] > 12) \ + && (WH3l_mOSll[2] < 0 || WH3l_mOSll[2] > 12) \ + && abs(WH3l_chlll) == 1 \ + ' + +cuts['zmass_cut'] = 'WH3l_ZVeto < 25' + +cuts['jet_cut_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + ' + +cuts['bveto_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + ' + +cuts['z4lveto_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + ' + +cuts['zh3l_SR_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetjj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetjj > 3.14159/2 \ + ' + +cuts['met_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + ' + +cuts['zh3l_SR_2j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetjj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_2j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetjj > 3.14159/2 \ + ' + +cuts['jet_cut_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + ' + +cuts['bveto_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + ' + +cuts['z4lveto_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + ' + +cuts['zh3l_SR_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetj > 3.14159/2 \ + ' + +cuts['met_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + ' + +cuts['zh3l_SR_1j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_1j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetj > 3.14159/2 \ + ' + +''' + #11 = e +# 13 = mu +# 15 = tau +''' diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/ch_lll.cc b/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/ch_lll.cc new file mode 100644 index 00000000..de6ef009 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/ch_lll.cc @@ -0,0 +1,41 @@ +#ifndef whchlll +#define whchlll + + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" +#include "TLorentzVector.h" + +using namespace ROOT; +using namespace ROOT::VecOps; + +float ch_lll(int nLepton, + RVecF Lepton_pt, + RVecF Lepton_eta, + RVecF Lepton_phi, + RVecF Lepton_pdgId){ + + + // Create default value + float ch_lll = -9999.0; + + // Check that we have at least 3 good leptons + if (nLepton < 3) return ch_lll; + + bool WH3l_ok = abs( abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2] ) <= 1; + if (!WH3l_ok) return ch_lll; + + ch_lll = abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2]; + + return ch_lll; +} + + +#endif diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/flag_ossf.cc b/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/flag_ossf.cc new file mode 100644 index 00000000..a46acea2 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/flag_ossf.cc @@ -0,0 +1,57 @@ +#ifndef flagossf +#define flagossf + + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" +#include "TLorentzVector.h" + +using namespace ROOT; +using namespace ROOT::VecOps; + +bool flag_ossf(int nLepton, + RVecF Lepton_pt, + RVecF Lepton_eta, + RVecF Lepton_phi, + RVecF Lepton_pdgId){ + + // Create default value + bool flag_OSSF = false; + + // Check that we have at least 3 good leptons + if (nLepton < 3) return flag_OSSF; + + bool WH3l_ok = abs( abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2] ) <= 1; + if (!WH3l_ok) return flag_OSSF; + + // Initialize leptons 4-vectors + std::vector leptons_vector = { + ROOT::Math::PtEtaPhiMVector(Lepton_pt[0],Lepton_eta[0],Lepton_phi[0],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[1],Lepton_eta[1],Lepton_phi[1],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[2],Lepton_eta[2],Lepton_phi[2],0) + }; + + // Compute minimum difference |mll - mZ| + float minmllDiffToZ = 9999.0; + for (uint i = 0; i < 3; i++){ + for (uint j = i+1; j < 3; j++){ + if ( Lepton_pdgId[i] + Lepton_pdgId[j] != 0 ) continue; + float mllDiffToZ = abs( (leptons_vector[i] + leptons_vector[j]).M() - 91.1876 ); + if ( mllDiffToZ < minmllDiffToZ ) minmllDiffToZ = mllDiffToZ; + } + } + + if (minmllDiffToZ != 9999.0) flag_OSSF = true; + + return flag_OSSF; +} + + +#endif diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/mOS_ll.cc b/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/mOS_ll.cc new file mode 100644 index 00000000..1ec3c638 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/mOS_ll.cc @@ -0,0 +1,54 @@ +#ifndef mosll +#define mosll + + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" +#include "TLorentzVector.h" + +using namespace ROOT; +using namespace ROOT::VecOps; + +ROOT::RVecF mOS_ll(int nLepton, + RVecF Lepton_pt, + RVecF Lepton_eta, + RVecF Lepton_phi, + RVecF Lepton_pdgId){ + + // Create default value + ROOT::RVecF mOSll_vector = {-9999.0, -9999.0, -9999.0}; + + // Check that we have at least 3 good leptons + if (nLepton < 3) return mOSll_vector; + + bool WH3l_ok = abs( abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2] ) <= 1; + if (!WH3l_ok) return mOSll_vector; + + // Initialize leptons 4-vectors + std::vector leptons_vector = { + ROOT::Math::PtEtaPhiMVector(Lepton_pt[0],Lepton_eta[0],Lepton_phi[0],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[1],Lepton_eta[1],Lepton_phi[1],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[2],Lepton_eta[2],Lepton_phi[2],0) + }; + + // Loop over the three leptons + for (uint i = 0; i < 3; i++){ + for (uint j = i+1; j < 3; j++){ + if (Lepton_pdgId[i]*Lepton_pdgId[j] < 0) + mOSll_vector.push_back( (leptons_vector[i]+leptons_vector[j]).M() ); + else + mOSll_vector.push_back(-9999.0); + } + } + return mOSll_vector; +} + + +#endif diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/m_lll.cc b/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/m_lll.cc new file mode 100644 index 00000000..46ce151b --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/m_lll.cc @@ -0,0 +1,49 @@ +#ifndef whmlll +#define whmlll + + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" +#include "TLorentzVector.h" + +using namespace ROOT; +using namespace ROOT::VecOps; + +float m_lll(int nLepton, + RVecF Lepton_pt, + RVecF Lepton_eta, + RVecF Lepton_phi, + RVecF Lepton_pdgId){ + + + // Create default value + float mlll = -9999.0; + + // Check that we have at least 3 good leptons + if (nLepton < 3) return mlll; + + bool WH3l_ok = abs( abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2] ) <= 1; + if (!WH3l_ok) return mlll; + + // Initialize leptons 4-vectors + std::vector leptons_vector = { + ROOT::Math::PtEtaPhiMVector(Lepton_pt[0],Lepton_eta[0],Lepton_phi[0],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[1],Lepton_eta[1],Lepton_phi[1],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[2],Lepton_eta[2],Lepton_phi[2],0) + }; + + mlll = (leptons_vector[0] + leptons_vector[1] + leptons_vector[2]).M(); + + return mlll; + +} + + +#endif diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/z_veto.cc b/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/z_veto.cc new file mode 100644 index 00000000..a527c0c4 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/macros/z_veto.cc @@ -0,0 +1,56 @@ +#ifndef zveto +#define zveto + + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" +#include "TLorentzVector.h" + +using namespace ROOT; +using namespace ROOT::VecOps; + +float z_veto(int nLepton, + RVecF Lepton_pt, + RVecF Lepton_eta, + RVecF Lepton_phi, + RVecF Lepton_pdgId){ + + + // Create default value + float z_veto = 9999.0; + + // Check that we have at least 3 good leptons + if (nLepton < 3) return z_veto; + + bool WH3l_ok = abs( abs(Lepton_pdgId[0])/Lepton_pdgId[0] + abs(Lepton_pdgId[1])/Lepton_pdgId[1] + abs(Lepton_pdgId[2])/Lepton_pdgId[2] ) <= 1; + if (!WH3l_ok) return z_veto; + + // Initialize leptons 4-vectors + std::vector leptons_vector = { + ROOT::Math::PtEtaPhiMVector(Lepton_pt[0],Lepton_eta[0],Lepton_phi[0],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[1],Lepton_eta[1],Lepton_phi[1],0), + ROOT::Math::PtEtaPhiMVector(Lepton_pt[2],Lepton_eta[2],Lepton_phi[2],0) + }; + + // Compute minimum difference |mll - mZ| + float minmllDiffToZ = 9999.0; + for (uint i = 0; i < 3; i++){ + for (uint j = i+1; j < 3; j++){ + if ( Lepton_pdgId[i] + Lepton_pdgId[j] != 0 ) continue; + float mllDiffToZ = abs( (leptons_vector[i] + leptons_vector[j]).M() - 91.1876 ); + if ( mllDiffToZ < minmllDiffToZ ) minmllDiffToZ = mllDiffToZ; + } + } + + return minmllDiffToZ; +} + + +#endif diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/ngenjet.cc b/ZH3l_BDT/zh3l_run2_bdt_rdf/ngenjet.cc new file mode 100644 index 00000000..99893569 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/ngenjet.cc @@ -0,0 +1,121 @@ + +#ifndef ngenjet +#define ngenjet + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" + +using namespace ROOT; +using namespace ROOT::VecOps; +double CountGenJet( + int nLeptonGen, + RVecB LeptonGen_isPrompt, + RVecI LeptonGen_pdgId, + RVecF LeptonGen_pt, + RVecF LeptonGen_eta, + RVecF LeptonGen_phi, + RVecF LeptonGen_mass, + int nPhotonGen, + RVecF PhotonGen_pt, + RVecF PhotonGen_eta, + RVecF PhotonGen_phi, + RVecF PhotonGen_mass, + int nGenJet, + RVecF GenJet_pt, + RVecF GenJet_eta, + RVecF GenJet_phi + ){ + unsigned nJ = nGenJet; + + unsigned nL = nLeptonGen; + + std::vector iPromptL{}; + iPromptL.reserve(nL); + + for (unsigned iL{0}; iL != nL; ++iL) { + if (!LeptonGen_isPrompt[iL]) + continue; + + unsigned absId{static_cast(std::abs(LeptonGen_pdgId[iL]))}; + if (absId != 11 && absId != 13) + continue; + + iPromptL.push_back(iL); + } + + if (iPromptL.size() == 0) { + unsigned n{0}; + for (unsigned iJ{0}; iJ != nJ; ++iJ) { + if (GenJet_pt[iJ] > 30.) + ++n; + } + return n; + } + + std::vector dressedLeptons{}; + for (unsigned iL : iPromptL) { + dressedLeptons.push_back( + ROOT::Math::PtEtaPhiMVector( + LeptonGen_pt[iL], + LeptonGen_eta[iL], + LeptonGen_phi[iL], + LeptonGen_mass[iL] + ) + ); + } + + unsigned nP = nPhotonGen; + + for (unsigned iP{0}; iP != nP; ++iP) { + double minDR2{1000.}; + int iDMin{-1}; + for (unsigned iD{0}; iD != iPromptL.size(); ++iD) { + unsigned iL{iPromptL[iD]}; + double dEta{LeptonGen_eta[iL] - PhotonGen_eta[iP]}; + double dPhi{TVector2::Phi_mpi_pi(LeptonGen_phi[iL] - PhotonGen_phi[iP])}; + double dR2{dEta * dEta + dPhi * dPhi}; + if (dR2 < minDR2) { + minDR2 = dR2; + iDMin = iD; + } + } + + if (minDR2 < 0.09) + dressedLeptons[iDMin] += ROOT::Math::PtEtaPhiMVector( + PhotonGen_pt[iP], + PhotonGen_eta[iP], + PhotonGen_phi[iP], + PhotonGen_mass[iP]); + } + + unsigned n{0}; + for (unsigned iJ{0}; iJ != nJ; ++iJ) { + if (GenJet_pt[iJ] < 30.) + continue; + + bool overlap{false}; + for (auto& p4 : dressedLeptons) { + if (p4.pt() < 10.) + continue; + + double dEta{p4.eta() - GenJet_eta[iJ]}; + double dPhi{TVector2::Phi_mpi_pi(p4.phi() - GenJet_phi[iJ])}; + if (dEta * dEta + dPhi * dPhi < 0.016) { + overlap = true; + break; + } + } + if (!overlap) + ++n; + } + return n; +} + +#endif \ No newline at end of file diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/nuisances.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/nuisances.py new file mode 100644 index 00000000..2dc2d2b2 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/nuisances.py @@ -0,0 +1,580 @@ +# nuisances +# name of samples here must match keys in samples.py + +try: + mc = [skey for skey in samples if skey != 'DATA' and not skey.startswith('Fake')] +except NameError: + mc = [] + +try: + fitcuts = [cut for cut in cuts if 'SR' in cut or 'CR' in cut] +except NameError: + fitcuts = [] + +nuisances = {} + +#### Luminosity + +nuisances['lumi_Uncorrelated'] = { + 'name': 'lumi_13TeV_2018', + 'type': 'lnN', + 'samples': dict((skey, '1.015') for skey in mc if skey not in ['WZ']), + 'cuts' : fitcuts +} + +nuisances['lumi_XYFact'] = { + 'name': 'lumi_13TeV_XYFact', + 'type': 'lnN', + 'samples': dict((skey, '1.02') for skey in mc if skey not in ['WZ']), + 'cuts' : fitcuts +} + +nuisances['lumi_LScale'] = { + 'name': 'lumi_13TeV_LSCale', + 'type': 'lnN', + 'samples': dict((skey, '1.002') for skey in mc if skey not in ['WZ']), + 'cuts' : fitcuts +} + +nuisances['lumi_CurrCalib'] = { + 'name': 'lumi_13TeV_CurrCalib', + 'type': 'lnN', + 'samples': dict((skey, '1.002') for skey in mc if skey not in ['WZ']), + 'cuts' : fitcuts +} + +#### Theoretical Systematics +# Commenting out as mkShapesRDF does not have LatinoAnalysis tools' HiggsXSection module (DS, 03Nov25). +# Scale +# from LatinoAnalysis.Tools.HiggsXSection import * +# HiggsXS = HiggsXSection() + +# nuisances['QCDscale_VH'] = { +# 'name' : 'QCDscale_VH', +# 'samples' : { +# 'WH_hww' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','WH','125.09','scale','sm'), +# 'ZH_hww' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ZH','125.09','scale','sm'), +# 'WH_htt' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','WH','125.09','scale','sm'), +# 'ZH_htt' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ZH','125.09','scale','sm') +# }, +# 'type' : 'lnN', +# 'cuts' : fitcuts +# } + +# nuisances['QCDscale_ggZH'] = { +# 'name' : 'QCDscale_ggZH', +# 'samples' : { +# 'ggZH_hww': HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ggZH','125.09','scale','sm'), +# }, +# 'type' : 'lnN', +# 'cuts' : fitcuts +# } + +# nuisances['QCDscale_ttH'] = { +# 'name' : 'QCDscale_ttH', +# 'samples' : { +# 'ttH_hww': HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ttH','125.09','scale','sm'), +# }, +# 'type' : 'lnN', +# 'cuts' : fitcuts +# } + +variations = ['Alt(LHEScaleWeight, 0, 1)', 'Alt(LHEScaleWeight, 1, 1)', 'Alt(LHEScaleWeight, 3, 1)', 'Alt(LHEScaleWeight, 5, 1)', 'Alt(LHEScaleWeight, 7, 1)', 'Alt(LHEScaleWeight, 8, 1)'] +# variations = ['Alt$(LHEScaleWeight[0],1)', 'Alt$(LHEScaleWeight[1],1)', 'Alt$(LHEScaleWeight[3],1)', 'Alt$(LHEScaleWeight[5],1)', 'Alt$(LHEScaleWeight[7],1)', 'Alt$(LHEScaleWeight[8],1)'] + +# Commenting out as mkShapesRDF could not find variations for the QCDscale nuisances (DS, 05Nov25). +# nuisances['QCDscale_V'] = { +# 'name': 'QCDscale_V', +# 'kind': 'weight_envelope', +# 'type': 'shape', +# 'samples': {'DY': variations}, +# 'AsLnN': '1', +# 'cuts' : fitcuts +# } + +# nuisances['QCDscale_VV'] = { +# 'name': 'QCDscale_VV', +# 'kind': 'weight_envelope', +# 'type': 'shape', +# 'samples': { +# 'WW' : variations, +# 'Zg' : variations, +# 'ZgS' : variations, +# 'WZ' : variations, +# 'ZZ' : variations +# }, +# 'cuts' : fitcuts +# } + +nuisances['QCDscale_WWewk'] = { + 'name': 'QCDscale_WWewk', + 'samples': { + 'WWewk': '1.11', + }, + 'type': 'lnN', + 'cuts' : fitcuts +} + + +# ggww and interference +nuisances['QCDscale_ggVV'] = { + 'name': 'QCDscale_ggVV', + 'type': 'lnN', + 'samples': { + 'ggWW': '1.15', + }, + 'cuts' : fitcuts +} + +nuisances['QCDscale_qqbar_ACCEPT'] = { + 'name' : 'QCDscale_qqbar_ACCEPT', + 'type' : 'lnN', + 'samples' : { + 'WH_hww' : '1.010', + 'ZH_hww' : '1.015', + 'WH_htt' : '1.010', + 'ZH_htt' : '1.015', + }, + 'cuts' : fitcuts +} + +nuisances['QCDscale_gg_ACCEPT'] = { + 'name' : 'QCDscale_gg_ACCEPT', + 'samples' : { + 'ggZH_hww': '1.012', + 'ggWW' : '1.012' + }, + 'type' : 'lnN', + 'cuts' : fitcuts +} + +# Commenting out as mkShapesRDF does not have LatinoAnalysis tools' HiggsXSection module (DS, 03Nov25). +# pdf +# nuisances['pdf_Higgs_gg'] = { +# 'name' : 'pdf_Higgs_gg', +# 'samples' : { +# 'ggZH_hww': HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ggZH','125.09','pdf','sm'), +# }, +# 'type' : 'lnN', +# 'cuts' : fitcuts +# } + +# nuisances['pdf_Higgs_qqbar'] = { +# 'name' : 'pdf_Higgs_qqbar', +# 'type' : 'lnN', +# 'samples' : { +# 'WH_hww' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','WH' ,'125.09','pdf','sm'), +# 'ZH_hww' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ZH' ,'125.09','pdf','sm'), +# 'WH_htt' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','WH' ,'125.09','pdf','sm'), +# 'ZH_htt' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ZH' ,'125.09','pdf','sm'), +# }, +# 'cuts' : fitcuts +# } + +# nuisances['pdf_Higgs_ttH'] = { +# 'name': 'pdf_Higgs_ttH', +# 'type': 'lnN', +# 'samples': { +# 'ttH_hww' : HiggsXS.GetHiggsProdXSNP('YR4','13TeV','ttH','125.09','pdf','sm') +# }, +# 'cuts' : fitcuts +# } + +nuisances['pdf_qqbar'] = { + 'name': 'pdf_qqbar', + 'type': 'lnN', + 'samples': { + 'Zg': '1.04', + 'ZgS': '1.04', + 'WZ': '1.04', + 'ZZ': '1.04', + }, + 'cuts' : fitcuts + } + +nuisances['pdf_Higgs_gg_ACCEPT'] = { + 'name' : 'pdf_Higgs_gg_ACCEPT', + 'samples' : { + 'ggZH_hww': '1.006', + }, + 'type' : 'lnN', + 'cuts' : fitcuts + } + +nuisances['pdf_Higgs_qqbar_ACCEPT'] = { + 'name' : 'pdf_Higgs_qqbar_ACCEPT', + 'type' : 'lnN', + 'samples' : { + 'WH_hww' : '1.003', + 'ZH_hww' : '1.002', + 'WH_htt' : '1.003', + 'ZH_htt' : '1.002', + }, + 'cuts' : fitcuts + } + +nuisances['pdf_qqbar_ACCEPT'] = { + 'name' : 'pdf_qqbar_ACCEPT', + 'type' : 'lnN', + 'samples' : { + 'WZ' : '1.001', + 'ZZ' : '1.001', + }, + 'cuts' : fitcuts + } + +nuisances['pdf_gg_ACCEPT'] = { + 'name': 'pdf_gg_ACCEPT', + 'samples': { + 'ggWW': '1.006', + }, + 'type': 'lnN', + 'cuts' : fitcuts +} + +# nuisances['PS_ISR'] = { +# 'name': 'PS_ISR', +# 'kind': 'weight', +# 'type': 'shape', +# 'samples': { +# 'ggZH_hww': ['1.066107*(nCleanGenJet==0) + 1.047857*(nCleanGenJet==1) + 1.030005*(nCleanGenJet==2) + 1.005028*(nCleanGenJet>=3)', '0.921874*(nCleanGenJet==0) + 0.941939*(nCleanGenJet==1) + 0.962282*(nCleanGenJet==2) + 0.991580*(nCleanGenJet>=3)'], +# 'ZH_hww': ['1.000684*(nCleanGenJet==0) + 1.000924*(nCleanGenJet==1) + 1.001683*(nCleanGenJet==2) + 1.002104*(nCleanGenJet>=3)', '0.999150*(nCleanGenJet==0) + 0.998821*(nCleanGenJet==1) + 0.997859*(nCleanGenJet==2) + 0.997316*(nCleanGenJet>=3)'], +# 'WZ': ['1.002552*(nCleanGenJet==0) + 1.010286*(nCleanGenJet==1) + 1.014420*(nCleanGenJet==2) + 1.006226*(nCleanGenJet>=3)', '0.996802*(nCleanGenJet==0) + 0.987227*(nCleanGenJet==1) + 0.982005*(nCleanGenJet==2) + 0.992030*(nCleanGenJet>=3)'], +# 'ZZ': ['1.003210*(nCleanGenJet==0) + 1.005480*(nCleanGenJet==1) + 1.004674*(nCleanGenJet==2) + 0.987845*(nCleanGenJet>=3)', '0.995997*(nCleanGenJet==0) + 0.993056*(nCleanGenJet==1) + 0.993659*(nCleanGenJet==2) + 1.014695*(nCleanGenJet>=3)'], +# }, +# 'cuts' : fitcuts +# } + +# nuisances['PS_FSR'] = { +# 'name': 'PS_FSR', +# 'kind': 'weight', +# 'type': 'shape', +# 'samples': { +# 'ggZH_hww': ['0.987316*(nCleanGenJet==0) + 0.986764*(nCleanGenJet==1) + 0.996498*(nCleanGenJet==2) + 1.004161*(nCleanGenJet>=3)', '1.019871*(nCleanGenJet==0) + 1.013853*(nCleanGenJet==1) + 1.005229*(nCleanGenJet==2) + 0.998573*(nCleanGenJet>=3)'], +# 'ZH_hww': ['0.992867*(nCleanGenJet==0) + 0.992845*(nCleanGenJet==1) + 0.999470*(nCleanGenJet==2) + 1.007245*(nCleanGenJet>=3)', '1.012465*(nCleanGenJet==0) + 1.012743*(nCleanGenJet==1) + 1.003215*(nCleanGenJet==2) + 0.991286*(nCleanGenJet>=3)'], +# 'WZ': ['0.992987*(nCleanGenJet==0) + 0.993725*(nCleanGenJet==1) + 1.000617*(nCleanGenJet==2) + 1.010869*(nCleanGenJet>=3)', '1.011267*(nCleanGenJet==0) + 1.010097*(nCleanGenJet==1) + 0.999445*(nCleanGenJet==2) + 0.983609*(nCleanGenJet>=3)'], +# 'ZZ': ['0.997245*(nCleanGenJet==0) + 0.998689*(nCleanGenJet==1) + 1.004475*(nCleanGenJet==2) + 1.011440*(nCleanGenJet>=3)', '1.004482*(nCleanGenJet==0) + 1.002081*(nCleanGenJet==1) + 0.992617*(nCleanGenJet==2) + 0.981314*(nCleanGenJet>=3)'], +# }, +# 'cuts' : fitcuts +# } + +nuisances['PU'] = { + 'name': 'CMS_PU_2018', + 'kind': 'weight', + 'type': 'shape', + 'samples': { + 'WZ': ['1.00103945*(puWeightUp/puWeight)', '0.99898154*(puWeightDown/puWeight)'], + 'ZZ': ['1.00125684*(puWeightUp/puWeight)', '0.99878368*(puWeightDown/puWeight)'], + 'ZH_hww': ['1.00129463*(puWeightUp/puWeight)', '0.99847264*(puWeightDown/puWeight)'], + 'ggZH_hww': ['0.99973837*(puWeightUp/puWeight)', '1.0001526*(puWeightDown/puWeight)'], + }, + 'AsLnN': '1', + 'cuts' : fitcuts +} + +### PU ID SF uncertainty +puid_syst = ['Jet_PUIDSF_up/Jet_PUIDSF', 'Jet_PUIDSF_down/Jet_PUIDSF'] + +nuisances['jetPUID'] = { + 'name': 'CMS_PUID_2018', + 'kind': 'weight', + 'type': 'shape', + 'samples': dict((skey, puid_syst) for skey in mc) +} + +nuisances['UE_whss'] = { + 'name' : 'UE_whss', + 'skipCMS' : 1, + 'type' : 'lnN', + 'samples' : { + 'WH_hww' : '1.015', + 'ZH_hww' : '1.015', + 'ggZH_hww' : '1.015', + 'WH_htt' : '1.015', + 'ZH_htt' : '1.015', + }, + 'cuts' : fitcuts +} + +nuisances['WZ3l2jnorm'] = { + 'name' : 'CMS_hww_WZ3l2jnorm', + 'samples' : { + 'WZ' : '1.00', + }, + 'type' : 'rateParam', + 'cuts' : [ + 'zh3l_WZ_CR_2j', + 'zh3l_WZ_CR_2j_met', + 'zh3l_SR_2j', + 'zh3l_SR_2j_met', + ] + } + +nuisances['WZ3l1jnorm'] = { + 'name' : 'CMS_hww_WZ3l1jnorm', + 'samples' : { + 'WZ' : '1.00', + }, + 'type' : 'rateParam', + 'cuts' : [ + 'zh3l_WZ_CR_1j', + 'zh3l_WZ_CR_1j_met', + 'zh3l_SR_1j', + 'zh3l_SR_1j_met', + ] + } + +#### Top + +apply_on = { + 'top': [ + '(topGenPt * antitopGenPt <= 0.) * 1.0816 + (topGenPt * antitopGenPt > 0.)', + '(topGenPt * antitopGenPt <= 0.) * 0.9184 + (topGenPt * antitopGenPt > 0.)' + ] +} + +nuisances['singleTopToTTbar'] = { + 'name': 'singleTopToTTbar', + 'skipCMS': 1, + 'kind': 'weight', + 'type': 'shape', + 'samples': apply_on, + 'cuts' : fitcuts +} + +nuisances['TopPtRew'] = { + 'name': 'CMS_topPtRew', # Theory uncertainty + 'kind': 'weight', + 'type': 'shape', + 'samples': {'top': ["1.", "1./Top_pTrw"]}, + 'symmetrize': True, + 'cuts' : fitcuts +} + +#### FAKES + +fakeW_EleUp = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lElUp / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_EleDown = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lElDown / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_MuUp = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lMuUp / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_MuDown = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lMuDown / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_statEleUp = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lstatElUp / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_statEleDown = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lstatElDown / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_statMuUp = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lstatMuUp / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' +fakeW_statMuDown = '( fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3lstatMuDown / fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_3l )' + +nuisances['fake_syst_e'] = { + 'name' : 'CMS_fake_syst_e', + 'type' : 'lnN', + 'samples' : { + 'Fake_e' : '1.30', + }, + 'cuts' : fitcuts +} + +nuisances['fake_syst_m'] = { + 'name' : 'CMS_fake_syst_m', + 'type' : 'lnN', + 'samples' : { + 'Fake_m' : '1.30', + }, + 'cuts' : fitcuts +} + +nuisances['fake_ele'] = { + 'name' : 'CMS_fake_e_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : { + 'Fake' : [ fakeW_EleUp , fakeW_EleDown ], + }, + 'cuts' : fitcuts +} + +nuisances['fake_ele_stat'] = { + 'name' : 'CMS_fake_stat_e_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : { + 'Fake' : [ fakeW_statEleUp , fakeW_statEleDown ], + }, + 'cuts' : fitcuts +} + +nuisances['fake_mu'] = { + 'name' : 'CMS_fake_m_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : { + 'Fake' : [ fakeW_MuUp , fakeW_MuDown ], + }, + 'cuts' : fitcuts +} + +nuisances['fake_mu_stat'] = { + 'name' : 'CMS_fake_stat_m_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : { + 'Fake' : [ fakeW_statMuUp , fakeW_statMuDown ], + }, + 'cuts' : fitcuts +} + +###### B-tagger + +for shift in ['jes', 'lf', 'hf', 'hfstats1', 'hfstats2', 'lfstats1', 'lfstats2', 'cferr1', 'cferr2']: + btag_syst = ['(btagSF%sup)/(btagSF)' % shift, '(btagSF%sdown)/(btagSF)' % shift] + + name = 'CMS_btag_%s' % shift + if 'stats' in shift: + name += '_2018' + + nuisances['btag_shape_%s' % shift] = { + 'name': name, + 'kind': 'weight', + 'type': 'shape', + 'samples': dict((skey, btag_syst) for skey in mc), + 'cuts' : fitcuts + } + +#### Trigger Efficiency + +trig_syst = ['((TriggerEffWeight_3l_u)/(TriggerEffWeight_3l))*(TriggerEffWeight_3l>0.02) + (TriggerEffWeight_3l<=0.02)', '(TriggerEffWeight_3l_d)/(TriggerEffWeight_3l)'] + +nuisances['trigg'] = { + 'name' : 'CMS_eff_hwwtrigger_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : dict((skey, trig_syst) for skey in mc), + 'cuts' : fitcuts +} + +##### Electron Efficiency and energy scale + +id_syst_ele = [ 'SFweightEleUp', 'SFweightEleDown'] + +nuisances['eff_e'] = { + 'name' : 'CMS_eff_e_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : dict((skey, id_syst_ele) for skey in mc), + 'cuts' : fitcuts +} + +nuisances['electronpt'] = { + 'name' : 'CMS_scale_e_2018', + 'kind' : 'suffix', + 'type' : 'shape', + 'mapUp' : 'ElepTup', + 'mapDown' : 'ElepTdo', + 'samples' : dict((skey, ['1', '1']) for skey in mc), + 'folderUp' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__ElepTup_suffix', + 'folderDown' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__ElepTdo_suffix', + 'AsLnN' : '1', + 'cuts' : fitcuts +} + +###### Muon Efficiency and energy scale + +id_syst_mu = [ 'SFweightMuUp', 'SFweightMuDown'] + +nuisances['eff_m'] = { + 'name' : 'CMS_eff_m_2018', + 'kind' : 'weight', + 'type' : 'shape', + 'samples' : dict((skey, id_syst_mu) for skey in mc), + 'cuts' : fitcuts +} + +nuisances['muonpt'] = { + 'name' : 'CMS_scale_m_2018', + 'kind' : 'suffix', + 'type' : 'shape', + 'mapUp' : 'MupTup', + 'mapDown' : 'MupTdo', + 'samples' : dict((skey, ['1', '1']) for skey in mc), + 'folderUp' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__MupTup_suffix', + 'folderDown' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__MupTdo_suffix', + 'AsLnN' : '1', + 'cuts' : fitcuts +} + +####### Jet energy scale + +jes_systs = ['JESAbsolute','JESAbsolute_2018','JESBBEC1','JESBBEC1_2018','JESEC2','JESEC2_2018','JESFlavorQCD','JESHF','JESHF_2018','JESRelativeBal','JESRelativeSample_2018'] +folderup = "" +folderdo = "" + +for js in jes_systs: + if 'Absolute' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESAbsoluteup_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESAbsolutedo_suffix' + elif 'BBEC1' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESBBEC1up_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESBBEC1do_suffix' + elif 'EC2' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESEC2up_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESEC2do_suffix' + elif 'HF' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESHFup_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESHFdo_suffix' + elif 'Relative' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESRelativeup_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESRelativedo_suffix' + elif 'FlavorQCD' in js: + folderup = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESFlavorQCDup_suffix' + folderdo = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JESFlavorQCDdo_suffix' + + nuisances[js] = { + 'name': 'CMS_scale_'+js, + 'kind': 'suffix', + 'type': 'shape', + 'mapUp': js+'up', + 'mapDown': js+'do', + 'samples': dict((skey, ['1', '1']) for skey in mc), + 'folderUp' : folderup, + 'folderDown' : folderdo, + 'AsLnN': '1', + 'cuts' : fitcuts + } + +###### Jet energy resolution +nuisances['JER'] = { + 'name' : 'CMS_res_j_2018', + 'kind': 'suffix', + 'type': 'shape', + 'mapUp': 'JERup', + 'mapDown': 'JERdo', + 'samples': dict((skey, ['1', '1']) for skey in mc), + 'folderUp': treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JERup_suffix', + 'folderDown': treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__JERdo_suffix', + 'AsLnN' : '1' +} +##### MET energy scale + +nuisances['met'] = { + 'name' : 'CMS_scale_met_2018', + 'kind' : 'suffix', + 'type' : 'shape', + 'mapUp' : 'METup', + 'mapDown' : 'METdo', + 'samples' : dict((skey, ['1', '1']) for skey in mc), + 'folderUp' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__METup_suffix', + 'folderDown' : treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7__METdo_suffix', + 'AsLnN' : '1', + 'cuts' : fitcuts +} + +# Use the following if you want to apply the automatic combine MC stat nuisances. +nuisances['stat'] = { + 'type' : 'auto', + 'maxPoiss' : '10', + 'includeSignal' : '1', + # nuisance ['maxPoiss'] = Number of threshold events for Poisson modelling + # nuisance ['includeSignal'] = Include MC stat nuisances on signal processes (1=True, 0=False) + 'samples' : {}, + 'cuts' : fitcuts + } + +for n in nuisances.values(): + n['skipCMS'] = 1 diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/plot.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/plot.py new file mode 100644 index 00000000..e53e2f8a --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/plot.py @@ -0,0 +1,249 @@ +# plot configuration +groupPlot = {} + +# Groups of samples to improve the plots. +# If not defined, normal plots is used + +groupPlot['DY'] = { + 'nameHR' : 'DY', + 'color': 616, # kMagenta + 'isSignal' : 0, + 'samples' : ['DY'] +} + +groupPlot['WW'] = { + 'nameHR' : 'WW', + 'color': 632, # kRed + 'isSignal' : 0, + 'samples' : ['WW'] +} + +groupPlot['Zg'] = { + 'nameHR' : 'Zg', + 'color': 859, # kAzure -1 + 'isSignal' : 0, + 'samples' : ['Zg'] + } + +groupPlot['ZgS'] = { + 'nameHR' : 'ZgS', + 'color': 432, # kCyan + 'isSignal' : 0, + 'samples' : ['ZgS'] + } + +groupPlot['WZ'] = { + 'nameHR' : "WZ", + 'isSignal' : 0, + 'color' : 400, # Yellow + 'samples' : ['WZ'] + } + +groupPlot['ZZ'] = { + 'nameHR' : "ZZ", + 'isSignal' : 0, + 'color' : 617, # kViolet + 1 + 'samples' : ['ZZ'] + } + +groupPlot['VVV'] = { + 'nameHR' : 'VVV', + 'isSignal' : 0, + 'color': 806, # kOrange + 6 + 'samples' : ['VVV'] + } + + +groupPlot['top'] = { + 'nameHR' : 'top', + 'color': 416, # kGreen + 'isSignal' : 0, + 'samples' : ['top'] +} + +groupPlot['ttV'] = { + 'nameHR' : 'ttV', + 'color': 419, # kGreen+3 + 'isSignal' : 0, + 'samples' : ['ttV'] +} + +groupPlot['Fake'] = { + 'nameHR' : 'Non-prompt', + 'isSignal' : 0, + 'color': 921, # kGray + 1 + 'samples' : ['Fake_e', 'Fake_m'] + } + +groupPlot['Higgs'] = { + 'nameHR' : 'Higgs', + 'isSignal' : 1, + 'color': 632, # kRed + 'scaleMultiplicativeOverlaid' : 10.0, + 'samples' : ['WH_hww', 'ZH_hww', 'ggZH_hww', 'ZH_htt', 'ttH_hww', 'WH_htt'] + } + +# Individual plots +plot = {} + +plot['DY'] = { + 'nameHR' : 'DY', + 'color': 858, # kAzure -2 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['WW'] = { + 'nameHR' : 'WW', + 'color': 858, # kAzure -2 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +#plot['WWewk'] = { +# 'nameHR' : 'WWewk', +# 'color': 858, # kAzure -2 +# 'isSignal' : 0, +# 'isData' : 0, +# 'scale' : 1.0 +# } + +#plot['ggWW'] = { +# 'nameHR' : 'ggWW', +# 'color': 858, # kAzure -2 +# 'isSignal' : 0, +# 'isData' : 0, +# 'scale' : 1.0 +# } + +plot['Zg'] = { + 'nameHR' : 'Zg', + 'color': 859, # kAzure -1 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['ZgS'] = { + 'nameHR' : 'ZgS', + 'color': 859, # kAzure -1 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['WZ'] = { + 'nameHR' : 'WZ', + 'color': 858, # kAzure -2 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + #'scale' : 0.89 #1j norm + #'scale' : 1.22 #2j norm + } + +plot['ZZ'] = { + 'nameHR' : 'ZZ', + 'color': 856, # kAzure -4 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['VVV'] = { + 'color': 857, # kAzure -3 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['top'] = { + 'color': 419, # kGreen+3 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 # ele/mu trigger efficiency datadriven +} + +plot['ttV'] = { + 'color': 419, # kGreen+3 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 # ele/mu trigger efficiency datadriven +} + +plot['Fake_e'] = { + 'color': 921, # kGray + 1 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['Fake_m'] = { + 'color': 921, # kGray + 1 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0 + } + +plot['ttH_hww'] = { + 'nameHR' : 'ttH', + 'color': 632+3, # kRed+3 + 'isSignal' : 1, + 'isData' : 0, + 'scale' : 1 + } + +plot['ZH_hww'] = { + 'nameHR' : 'ZH', + 'color': 632+3, # kRed+3 + 'isSignal' : 1, + 'isData' : 0, + 'scale' : 1 + } + +plot['ggZH_hww'] = { + 'nameHR' : 'ggZH', + 'color': 632+4, # kRed+4 + 'isSignal' : 1, + 'isData' : 0, + 'scale' : 1 + } + +plot['WH_hww'] = { + 'nameHR' : 'WH', + 'color': 632+2, # kRed+2 + 'isSignal' : 1, + 'isData' : 0, + 'scale' : 1 + } + +# plot['WH_htt'] = { +# 'nameHR' : 'WH htt', +# 'color': 632+1, # kRed+4 +# 'isSignal' : 1, +# 'isData' : 0, +# 'scale' : 1 +# } + +plot['ZH_htt'] = { + 'nameHR' : 'ZH htt', + 'color': 632+1, # kRed+4 + 'isSignal' : 1, + 'isData' : 0, + 'scale' : 1 + } + +plot['DATA'] = { + 'nameHR' : 'Data', + 'color': 1 , + 'isSignal' : 0, + 'isData' : 1 , + 'isBlind' : 0 + } + +# additional options +legend = {} +legend['lumi'] = 'L = 59.7/fb' +legend['sqrt'] = '#sqrt{s} = 13 TeV' diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/samples.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/samples.py new file mode 100644 index 00000000..6dbfabbf --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/samples.py @@ -0,0 +1,416 @@ +import os +import subprocess + +# global getSampleFiles +# from LatinoAnalysis.Tools.commonTools import getSampleFiles, addSampleWeight, getBaseWnAOD +# It looks like getBaseWnAOD and addSampleWeight from Latinos does the same job as CombineBaseW function from makeShapesRDF. Similarly getSampleFiles function from Latinos is the same as nanoGetSampleFiles from makeShapesRDF. + +from mkShapesRDF.lib.search_files import SearchFiles +searchFiles = SearchFiles() +redirector = "" +limitFiles = 3 +samples = {} + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + return [(name, _files[:limitFiles])] + else: + return [(name, _files)] + +def getSampleFilesNano(inputDir,Sample,absPath=False): + # return getSampleFiles(inputDir,Sample,absPath,'nanoLatino_') + return nanoGetSampleFiles(inputDir, Sample) # getSampleFiles replaced with nanoGetSampleFiles(path, name) (DS, 03Nov25) + +def CombineBaseW(samples, proc, samplelist): + _filtFiles = list(filter(lambda k: k[0] in samplelist, samples[proc]["name"])) + _files = list(map(lambda k: k[1], _filtFiles)) + _l = list(map(lambda k: len(k), _files)) + leastFiles = _files[_l.index(min(_l))] + dfSmall = ROOT.RDataFrame("Runs", leastFiles) + s = dfSmall.Sum("genEventSumw").GetValue() + f = ROOT.TFile(leastFiles[0]) + t = f.Get("Events") + t.GetEntry(1) + xs = t.baseW * s + + __files = [] + for f in _files: + __files += f + df = ROOT.RDataFrame("Runs", __files) + s = df.Sum("genEventSumw").GetValue() + newbaseW = str(xs / s) + return newbaseW # "/baseW is used after getBaseWnAOD/CombineBaseW calls in this code" + # weight = newbaseW + "/baseW" + + # for iSample in samplelist: + # addSampleWeight(samples, proc, iSample, weight) + + +def addSampleWeight(samples, sampleName, sampleNameType, weight): + obj = list(filter(lambda k: k[0] == sampleNameType, samples[sampleName]["name"]))[0] + samples[sampleName]["name"] = list( + filter(lambda k: k[0] != sampleNameType, samples[sampleName]["name"]) + ) + if len(obj) > 2: + samples[sampleName]["name"].append( + (obj[0], obj[1], obj[2] + "*(" + weight + ")") + ) + else: + samples[sampleName]["name"].append((obj[0], obj[1], "(" + weight + ")")) + + +############################################## +###### Tree Directory according to site ###### +############################################## + +SITE=os.uname()[1] +xrootdPath='' +if 'iihe' in SITE : + xrootdPath = 'dcap://maite.iihe.ac.be/' + treeBaseDir = '/pnfs/iihe/cms/store/user/xjanssen/HWW2015/' +elif 'cern' in SITE : + #xrootdPath='root://eoscms.cern.ch/' + treeBaseDir = '/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/' + +directory = treeBaseDir+'Autumn18_102X_nAODv7_Full2018v7/MCl1loose2018v7__MCCorr2018v7__l2loose__l2tightOR2018v7' + +################################################ +############ NUMBER OF LEPTONS ################# +################################################ + +#Nlep='2' +Nlep='3' +#Nlep='4' + +################################################ +############### Lepton WP ###################### +################################################ + +eleWP='mvaFall17V1Iso_WP90' +#eleWP='mvaFall17V1Iso_WP90_SS' +#eleWP='mvaFall17V2Iso_WP90' +#eleWP='mvaFall17V2Iso_WP90_SS' +muWP ='cut_Tight_HWWW' +eleWP_new = 'mvaFall17V1Iso_WP90_tthmva_70' +muWP_new = 'cut_Tight_HWWW_tthmva_80' + +LepWPCut = 'LepCut'+Nlep+'l__ele_'+eleWP_new+'__mu_'+muWP_new +#LepWPCut = 'LepCut'+Nlep+'l__ele_'+eleWP+'__mu_'+muWP +#LepWPweight = 'ttHMVA_SF_3l[0]' #SF for new WPs, defined in aliases +LepWPweight = 'LepSF'+Nlep+'l__ele_'+eleWP_new+'__mu_'+muWP_new + +################################################ +############ BASIC MC WEIGHTS ################## +################################################ + +XSWeight = 'XSWeight' +SFweight = 'SFweight'+Nlep+'l*'+LepWPweight+'*'+LepWPCut+'*Jet_PUIDSF' +PromptGenLepMatch = 'PromptGenLepMatch'+Nlep+'l' + +################################################ +############## FAKE WEIGHTS #################### +################################################ + +#eleWP_new = 'mvaFall17V1Iso_WP90_tthmva_70' +#muWP_new = 'cut_Tight_HWWW_tthmva_80' + +if Nlep == '2' : + fakeW = 'fakeW2l_ele_'+eleWP_new+'_mu_'+muWP_new + #fakeW = 'fakeW2l_ele_'+eleWP+'_mu_'+muWP +else: + fakeW = 'fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_'+Nlep+'l' + #fakeW = 'fakeW_ele_'+eleWP+'_mu_'+muWP+'_'+Nlep+'l' + +################################################ +############### B-Tag WP ###################### +################################################ + +SFweight += '*btagSF' #define in aliases.py + +################################################ +############ MET FILTERS ################### +################################################ + +METFilter_MC = 'METFilter_MC' +METFilter_DATA = 'METFilter_DATA' + +################################################ +############ DATA DECLARATION ################## +################################################ + +DataRun = [ + ['A','Run2018A-02Apr2020-v1'] , + ['B','Run2018B-02Apr2020-v1'] , + ['C','Run2018C-02Apr2020-v1'] , + ['D','Run2018D-02Apr2020-v1'] , + ] + +DataSets = ['MuonEG','DoubleMuon','SingleMuon','EGamma'] + +DataTrig = { + 'MuonEG' : 'Trigger_ElMu' , + 'DoubleMuon' : '!Trigger_ElMu && Trigger_dblMu' , + 'SingleMuon' : '!Trigger_ElMu && !Trigger_dblMu && Trigger_sngMu' , + 'EGamma' : '!Trigger_ElMu && !Trigger_dblMu && !Trigger_sngMu && (Trigger_sngEl || Trigger_dblEl)' , + } + +########################################### +############# BACKGROUNDS ############### +########################################### + +############ DY ############ + +ptllDYW_NLO = '(0.87*(gen_ptll<10)+(0.379119+0.099744*gen_ptll-0.00487351*gen_ptll**2+9.19509e-05*gen_ptll**3-6.0212e-07*gen_ptll**4)*(gen_ptll>=10 && gen_ptll<45)+(9.12137e-01+1.11957e-04*gen_ptll-3.15325e-06*gen_ptll**2-4.29708e-09*gen_ptll**3+3.35791e-11*gen_ptll**4)*(gen_ptll>=45 && gen_ptll<200) + 1*(gen_ptll>200))' +ptllDYW_LO = '((0.632927+0.0456956*gen_ptll-0.00154485*gen_ptll*gen_ptll+2.64397e-05*gen_ptll*gen_ptll*gen_ptll-2.19374e-07*gen_ptll*gen_ptll*gen_ptll*gen_ptll+6.99751e-10*gen_ptll*gen_ptll*gen_ptll*gen_ptll*gen_ptll)*(gen_ptll>0)*(gen_ptll<100)+(1.41713-0.00165342*gen_ptll)*(gen_ptll>=100)*(gen_ptll<300)+1*(gen_ptll>=300))' +Zgfilter = '( !(Sum(PhotonGen_isPrompt==1 && PhotonGen_pt>15 && abs(PhotonGen_eta)<2.6) > 0 && Sum(LeptonGen_isPrompt==1 && LeptonGen_pt>15)>=2) )' #Zg sample uses photon pt > 15, lepton pt > 15 + +samples['DY'] = { 'name' : getSampleFilesNano(directory,'DYJetsToLL_M-10to50-LO') + + getSampleFilesNano(directory,'DYJetsToLL_M-10to50-LO_ext1') + + getSampleFilesNano(directory,'DYJetsToLL_M-50-LO') + + getSampleFilesNano(directory, 'DYJetsToLL_M-50') + + getSampleFilesNano(directory, 'DYJetsToLL_M-50_ext2') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-100to200') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-200to400') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-400to600') + + getSampleFilesNano(directory,'DYJetsToLL_M-4to50_HT-600toInf') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-70to100') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-100to200') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-200to400') + # + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-400to600') + # + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-400to600_ext2') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-600to800') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-800to1200') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-1200to2500') + + getSampleFilesNano(directory,'DYJetsToLL_M-50_HT-2500toInf'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC +'*'+Zgfilter, + 'FilesPerJob' : 5, + 'suppressNegative' :['all'], + 'suppressNegativeNuisances' :['all'], + } + +M10baseW = '1'#CombineBaseW(directory,'Autumn18_102X_nAODv7_Full2018v7',['DYJetsToLL_M-10to50-LO','DYJetsToLL_M-10to50-LO_ext1']) +#HT400baseW = getBaseWnAOD(directory,'Autumn18_102X_nAODv7_Full2018v7',['DYJetsToLL_M-50_HT-400to600']) + +addSampleWeight(samples,'DY','DYJetsToLL_M-10to50-LO' ,ptllDYW_LO+'*(LHE_HT<100.0)*'+M10baseW+'/baseW') +addSampleWeight(samples,'DY','DYJetsToLL_M-10to50-LO_ext1' ,ptllDYW_LO+'*(LHE_HT<100.0)*'+M10baseW+'/baseW') +addSampleWeight(samples,'DY','DYJetsToLL_M-50-LO' ,ptllDYW_LO+'*(LHE_HT<70.0)') +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-100to200' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-200to400' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-400to600' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-4to50_HT-600toInf' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-70to100' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-100to200' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-200to400' ,ptllDYW_LO) +#addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-400to600' ,ptllDYW_LO+'*'+HT400baseW+'/baseW') +#addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-400to600_ext2',ptllDYW_LO+'*'+HT400baseW+'/baseW') +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-600to800' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-800to1200' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-1200to2500' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_HT-2500toInf' ,ptllDYW_LO) +addSampleWeight(samples,'DY', 'DYJetsToLL_M-50' ,ptllDYW_LO) +addSampleWeight(samples,'DY','DYJetsToLL_M-50_ext2' ,ptllDYW_LO) + +############ Top ############ + +samples['top'] = { 'name' : getSampleFilesNano(directory,'TTTo2L2Nu') + + getSampleFilesNano(directory,'ST_s-channel_ext1') + # + getSampleFilesNano(directory,'ST_t-channel_antitop') + + getSampleFilesNano(directory,'ST_t-channel_top') + + getSampleFilesNano(directory,'ST_tW_antitop_ext1') + + getSampleFilesNano(directory,'ST_tW_top_ext1'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + 'FilesPerJob' : 6, + } + +addSampleWeight(samples,'top','TTTo2L2Nu','Top_pTrw') + +samples['ttV'] = { 'name' : getSampleFilesNano(directory,'TTWJetsToLNu') + + getSampleFilesNano(directory,'TTZjets'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + 'FilesPerJob' : 5, + } + + +############ WW ############ + + +samples['WW'] = { 'name' : getSampleFilesNano(directory,'WWTo2L2Nu'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*nllW', + } + +#samples['WWewk'] = { 'name' : getSampleFilesNano(directory,'WpWmJJ_EWK_QCD_noTop_noHiggs') +# + getSampleFilesNano(directory,'WpWpJJ_EWK_QCD'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*(Sum$(abs(GenPart_pdgId)==6 || GenPart_pdgId==25)==0)*(lhe_mW1[0] > 60. && lhe_mW1[0] < 100. && lhe_mW2[0] > 60. && lhe_mW2[0] < 100.)', +# } + +#samples['ggWW'] = { 'name' : getSampleFilesNano(directory,'GluGluToWWToENEN') +# + getSampleFilesNano(directory,'GluGluToWWToENMN') +# + getSampleFilesNano(directory,'GluGluToWWToENTN') +# + getSampleFilesNano(directory,'GluGluToWWToMNEN') +# + getSampleFilesNano(directory,'GluGluToWWToMNMN') +# + getSampleFilesNano(directory,'GluGluToWWToMNTN') +# + getSampleFilesNano(directory,'GluGluToWWToTNEN') +# + getSampleFilesNano(directory,'GluGluToWWToTNMN') +# + getSampleFilesNano(directory,'GluGluToWWToTNTN'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*1.53/1.4', +# } + +###### Zg + +samples['Zg'] = { 'name' : getSampleFilesNano(directory,'ZGToLLG'), + 'weight' : XSWeight+'*'+SFweight+'*'+METFilter_MC + '*(Gen_ZGstar_mass <= 0)', + 'FilesPerJob' : 6 , + } + +###### Zg* + +samples['ZgS'] = { 'name' : getSampleFilesNano(directory,'ZGToLLG'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*(Gen_ZGstar_mass > 0)', + 'FilesPerJob' : 4 , + } + +##### WZ + +samples['WZ'] = { 'name': getSampleFilesNano(directory,'WZTo3LNu_mllmin01') + + getSampleFilesNano(directory,'WZTo2L2Q'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*(gstarHigh)' , + 'FilesPerJob' : 5 , + } + +##### ZZ + +samples['ZZ'] = { 'name' : getSampleFilesNano(directory,'ZZTo2L2Nu_ext1') + + getSampleFilesNano(directory,'ZZTo2L2Nu_ext2') + + getSampleFilesNano(directory,'ZZTo2L2Q') + + getSampleFilesNano(directory,'ZZTo4L_ext1') + + getSampleFilesNano(directory,'ZZTo4L_ext2') + #+ getSampleFilesNano(directory,'ggZZ4m') #Missing file for ElepTup + + getSampleFilesNano(directory,'ggZZ4m_ext1') + + getSampleFilesNano(directory,'ggZZ4t') + + getSampleFilesNano(directory,'ggZZ2e2t') + + getSampleFilesNano(directory,'ggZZ2m2t') + + getSampleFilesNano(directory,'ggZZ2e2m'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC, + 'FilesPerJob' : 3, + } + +ZZ2LbaseW = '1'#CombineBaseW(directory,'Autumn18_102X_nAODv7_Full2018v7',['ZZTo2L2Nu_ext1','ZZTo2L2Nu_ext2']) +ZZ4LbaseW = '1'#CombineBaseW(directory,'Autumn18_102X_nAODv7_Full2018v7',['ZZTo4L_ext1', 'ZZTo4L_ext2']) +#ggZZbaseW = getBaseWnAOD(directory,'Autumn18_102X_nAODv7_Full2018v7',['ggZZ4m', 'ggZZ4m_ext1']) + +addSampleWeight(samples,'ZZ','ZZTo2L2Nu_ext1',"1.07*"+ZZ2LbaseW+"/baseW") ## The non-ggZZ NNLO/NLO k-factor, cited from https://arxiv.org/abs/1405.2219v1 +addSampleWeight(samples,'ZZ','ZZTo2L2Nu_ext2',"1.07*"+ZZ2LbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ZZTo2L2Q', "1.07") +addSampleWeight(samples,'ZZ','ZZTo4L_ext1', "1.07*"+ZZ4LbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ZZTo4L_ext2', "1.07*"+ZZ4LbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ggZZ2e2t', "1.68") ## The NLO/LO k-factor, cited from https://arxiv.org/abs/1509.06734v1 +addSampleWeight(samples,'ZZ','ggZZ2m2t', "1.68") +addSampleWeight(samples,'ZZ','ggZZ2e2m', "1.68") +#addSampleWeight(samples,'ZZ','ggZZ4m', "1.68*"+ggZZbaseW+"/baseW") +addSampleWeight(samples,'ZZ','ggZZ4m_ext1', "1.68") +addSampleWeight(samples,'ZZ','ggZZ4t', "1.68") + +############ VVV ############ + +samples['VVV'] = { 'name' : getSampleFilesNano(directory,'ZZZ') + + getSampleFilesNano(directory,'WZZ') + + getSampleFilesNano(directory,'WWZ') + + getSampleFilesNano(directory,'WWW'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +########################################## +################ SIGNALS ################# +########################################## + +############ ZH H->WW ############ + +samples['ZH_hww'] = { 'name' : getSampleFilesNano(directory,'HZJ_HToWW_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +samples['ggZH_hww'] = { 'name' : getSampleFilesNano(directory,'GluGluZH_HToWW_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +############ WH H->WW ############ + +samples['WH_hww'] = { 'name' : getSampleFilesNano(directory,'HWplusJ_HToWW_M125') + + getSampleFilesNano(directory,'HWminusJ_HToWW_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +############ ttH ############ + +samples['ttH_hww'] = { 'name' : getSampleFilesNano(directory,'ttHToNonbb_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +############ bbH ############ +# Not available for Latinos 2016 v6 + +############ H->TauTau ############ + +#samples['ggH_htt'] = { 'name' : getSampleFilesNano(directory,'GluGluHToTauTau_M125'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , +# } + +#samples['qqH_htt'] = { 'name' : getSampleFilesNano(directory,'VBFHToTauTau_M125'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , +# } + +samples['ZH_htt'] = { 'name' : getSampleFilesNano(directory,'HZJ_HToTauTau_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +samples['WH_htt'] = { 'name' : getSampleFilesNano(directory,'HWplusJ_HToTauTau_M125') + + getSampleFilesNano(directory,'HWminusJ_HToTauTau_M125'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +########################################### +################## FAKE ################### +########################################### + +samples['Fake'] = { 'name': [ ] , + 'weight' : fakeW+'*'+METFilter_DATA, + 'weights' : [ ] , + 'isData': ['all'], + 'FilesPerJob' : 500 , + 'suppressNegativeNuisances' :['all'], + } + +directory = treeBaseDir+'Run2018_102X_nAODv7_Full2018v7/DATAl1loose2018v7__l2loose__fakeW/' +#directory = treeBaseDir+'Run2018_102X_nAODv6_Full2018v6/DATAl1loose2018v6__l2loose__fakeW/' +for Run in DataRun : + for DataSet in DataSets : + FileTarget = getSampleFilesNano(directory,DataSet+'_'+Run[1],True) + for iFile in FileTarget: + samples['Fake']['name'].append(iFile) + samples['Fake']['weights'].append(DataTrig[DataSet]) + +samples['Fake']['subsamples'] = { + 'e': 'abs(ZH3l_pdgid_l) == 11', + 'm': 'abs(ZH3l_pdgid_l) == 13' +} + +########################################### +################## DATA ################### +########################################### + +samples['DATA'] = { 'name': [ ] , + 'weight' : METFilter_DATA+'*'+LepWPCut, + 'weights' : [ ], + 'isData': ['all'], + 'FilesPerJob' : 500 , + } + +directory = treeBaseDir+'/Run2018_102X_nAODv7_Full2018v7/DATAl1loose2018v7__l2loose__l2tightOR2018v7/' +for Run in DataRun : + for DataSet in DataSets : + FileTarget = getSampleFilesNano(directory,DataSet+'_'+Run[1],True) + for iFile in FileTarget: + samples['DATA']['name'].append(iFile) + samples['DATA']['weights'].append(DataTrig[DataSet]) + diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/structure.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/structure.py new file mode 100644 index 00000000..44147676 --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/structure.py @@ -0,0 +1,113 @@ +# structure configuration for datacard +# keys here must match keys in samples.py +structure = {} + +# Backgrounds +structure['Fake_e'] = { + 'isSignal' : 0, + 'isData' : 0, + } + +structure['Fake_m'] = { + 'isSignal' : 0, + 'isData' : 0, + } + +structure['DY'] = { + 'isSignal' : 0, + 'isData' : 0, + 'removeFromCuts' : ['zh3l_SR_1j'] + } + +structure['Zg'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['ZgS'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['WW'] = { + 'isSignal' : 0, + 'isData' : 0, + 'removeFromCuts' : ['zh3l_SR_1j','zh3l_SR_2j'] + } + +#structure['WWewk'] = { +# 'isSignal' : 0, +# 'isData' : 0 +# } + +#structure['ggWW'] = { +# 'isSignal' : 0, +# 'isData' : 0 +# } + +structure['WZ'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['VVV'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['ZZ'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['ttV'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['top'] = { + 'isSignal' : 0, + 'isData' : 0, + 'removeFromCuts' : ['zh3l_WZ_CR_1j','zh3l_SR_1j'] + } + +# Signal +structure['WH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ZH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ggZH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ttH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['WH_htt'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +structure['ZH_htt'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +# Data +structure['DATA'] = { + 'isSignal' : 0, + 'isData' : 1 + } + + + + diff --git a/ZH3l_BDT/zh3l_run2_bdt_rdf/valid_data_samples.txt b/ZH3l_BDT/zh3l_run2_bdt_rdf/valid_data_samples.txt new file mode 100644 index 00000000..dfc044df --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/valid_data_samples.txt @@ -0,0 +1 @@ +/eos/user/d/dshekar/mkShapesRDF_rootfiles/ZH3l_BDTrun2/rootFile/mkShapes__ZH3l_BDTrun2_manualHADD2.root 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a/ZH3l_BDT/zh3l_run2_bdt_rdf/variables.py b/ZH3l_BDT/zh3l_run2_bdt_rdf/variables.py new file mode 100644 index 00000000..0ec4ca8a --- /dev/null +++ b/ZH3l_BDT/zh3l_run2_bdt_rdf/variables.py @@ -0,0 +1,129 @@ +# variables + +variables = {} + +#'fold' : # 0 = not fold (default), 1 = fold underflowbin, 2 = fold overflow bin, 3 = fold underflow and overflow +# The following is needed if combining plots between years: +#'doWeight' : 1 +#'binX' : 1 +#'binY' : <# of bins> + +variables['events'] = { 'name': '1', + 'range' : (1,0,2), + 'xaxis' : 'events', + 'fold' : 3 + } + +variables['njet'] = { 'name' : 'ZH3l_njet', + 'range' : (10,0,10), + 'xaxis' : 'N_{jet}', + 'fold' : 0 + } + +variables['met'] = { 'name' : 'PuppiMET_pt', + 'range' : (20,0,100), + 'xaxis' : 'Puppi MET', + 'fold' : 0 + } + +variables['pt1'] = { 'name': 'Lepton_pt[0]', + 'range' : (10,0.,200), + 'xaxis' : 'lept1_p_{T} [GeV]', + 'fold' : 0 + } + +variables['dphilmetj'] = { 'name' : 'ZH3l_dphilmetj', #FIXME + 'range' : (16,0,3.14159), + 'xaxis' : 'dphilmetj', + 'fold' : 0 + } + +variables['dphilmetjj'] = { 'name' : 'ZH3l_dphilmetjj', #FIXME + 'range' : (16,0,3.14159), + 'xaxis' : 'dphilmetjj', + 'fold' : 0, + 'cuts' : [cut for cut in cuts if '1j' not in cut] + } + +variables['pTlmetj'] = { 'name' : 'ZH3l_pTlmetj', + 'range' : (20,0,400), + 'xaxis' : 'pTlmetj', + 'fold' : 0 + } + +variables['pTlmetjj'] = { 'name' : 'ZH3l_pTlmetjj', + 'range' : (20,0,400), + 'xaxis' : 'pTlmetjj', + 'fold' : 0, + 'cuts' : [cut for cut in cuts if '1j' not in cut] + } + +variables['mTlmetjj'] = { 'name' : 'ZH3l_mTlmetjj', #FIXME + 'range' : (16,50,450), + 'xaxis' : 'mTlmetjj', + 'fold' : 3, + 'cuts' : [cut for cut in cuts if '1j' not in cut] + } + +variables['mTlmetj'] = { 'name' : 'ZH3l_mTlmetj', #FIXME + 'range' : (10,0,250), + 'xaxis' : 'mTlmetj', + 'fold' : 2, + } + +variables['ptz'] = { 'name' : 'ZH3l_pTZ', + 'range' : (20,0,400), + 'xaxis' : 'ptz', + 'fold' : 0 + } + +variables['mtw_notZ'] = { 'name' : 'ZH3l_mTlmet', #FIXME + 'range' : (20,0,200), + 'xaxis' : 'mTlmet', + 'fold' : 0 + } + +variables['mtw_fit'] = { 'name' : 'ZH3l_mTlmet', #FIXME + 'range' : (8,0,160), + 'xaxis' : 'mTlmet', + 'fold' : 2 + } + +variables['checkmZ'] = { 'name' : 'ZH3l_checkmZ', + 'range' : (20,0,200), + 'xaxis' : 'checkmZ', + 'fold' : 0 + } + +variables['ptjet0'] = { 'name' : 'CleanJet_pt[0]', + 'range' : (20,0,200), + 'xaxis' : 'Leading jet p_{T}', + 'fold' : 0 + } + +variables['ptjet1'] = { 'name' : 'CleanJet_pt[1]', + 'range' : (20,0,200), + 'xaxis' : 'Subleading jet p_{T}', + 'cuts' : [cut for cut in cuts if '1j' not in cut], + 'fold' : 0 + } + +variables['WlepId'] = { 'name' : 'ZH3l_pdgid_l', + 'range' : (31,-15.5,15.5), + 'xaxis' : 'W lepton ID', + 'fold' : 0 + } + +variables['flavor3l'] = { 'name' : '0*(abs(Lepton_pdgId[0])==13 && abs(Lepton_pdgId[1])==13 && abs(Lepton_pdgId[2])==13)+ \ + 1*(abs(Lepton_pdgId[0])==13 && abs(Lepton_pdgId[1])==13 && abs(Lepton_pdgId[2])==11)+ \ + 2*(abs(Lepton_pdgId[0])==13 && abs(Lepton_pdgId[1])==11 && abs(Lepton_pdgId[2])==13)+ \ + 3*(abs(Lepton_pdgId[0])==11 && abs(Lepton_pdgId[1])==13 && abs(Lepton_pdgId[2])==13)+ \ + 4*(abs(Lepton_pdgId[0])==13 && abs(Lepton_pdgId[1])==11 && abs(Lepton_pdgId[2])==11)+ \ + 5*(abs(Lepton_pdgId[0])==11 && abs(Lepton_pdgId[1])==13 && abs(Lepton_pdgId[2])==11)+ \ + 6*(abs(Lepton_pdgId[0])==11 && abs(Lepton_pdgId[1])==11 && abs(Lepton_pdgId[2])==13)+ \ + 7*(abs(Lepton_pdgId[0])==11 && abs(Lepton_pdgId[1])==11 && abs(Lepton_pdgId[2])==11)', + 'range' : (8,-0.5,7.5), + 'xaxis' : 'Trilepton flavor', + 'fold' : 0 + } + From a8989bcc271f9c70025e29326bee77929f691931 Mon Sep 17 00:00:00 2001 From: CodexForster Date: Wed, 28 Jan 2026 21:56:53 +0530 Subject: [PATCH 07/13] Addressing Z pT RW comments --- ZpTreweighting/aliases.py | 2 +- ZpTreweighting/extract_Zptrw.py | 235 ++++++++++++++++++-------------- ZpTreweighting/samples.py | 15 +- 3 files changed, 147 insertions(+), 105 deletions(-) diff --git a/ZpTreweighting/aliases.py b/ZpTreweighting/aliases.py index 12aee286..e56d90e0 100644 --- a/ZpTreweighting/aliases.py +++ b/ZpTreweighting/aliases.py @@ -33,7 +33,7 @@ aliases['gen_Zpt'] = { # 'linesToAdd': [".L /afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+"], - 'linesToAdd': ['#include "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc"'], + 'linesToAdd': ['.L /eos/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+'], 'class': 'GetGenZpt', 'args': 'nGenPart, GenPart_pt, GenPart_pdgId, GenPart_genPartIdxMother, GenPart_statusFlags, gen_ptll', # 'expr': 'gen_ptll', diff --git a/ZpTreweighting/extract_Zptrw.py b/ZpTreweighting/extract_Zptrw.py index 7cd229ea..4084c74d 100644 --- a/ZpTreweighting/extract_Zptrw.py +++ b/ZpTreweighting/extract_Zptrw.py @@ -1,16 +1,19 @@ +# ================================= +# Danush Shekar (UIC), 9Dec25 +# ================================= import ROOT import mplhep as hep import matplotlib.pyplot as plt import numpy as np - +from ROOT import TFitResultPtr # CMS plot style hep.style.use("CMS") style = hep.style.CMS -style["font.size"] = 13 +style["font.size"] = 18 plt.style.use(style) root_file = ROOT.TFile("mkShapes__ZpTreweighting.root") -zee_dir = root_file.Get("Zee_0j") +zee_dir = root_file.Get("Zmm_0j") ptll_dir = zee_dir.Get("ptll") histo_DY = ptll_dir.Get("histo_DY") @@ -29,100 +32,132 @@ histo_ratio = histo_trueData.Clone("histo_ratio") histo_ratio.Divide(histo_DY) -c = ROOT.TCanvas("c", "c", 800, 800) -c.Divide(1,2) - -# Top pad: main plot -pad1 = c.cd(1) -pad1.SetPad(0.0, 0.30, 1.0, 1.0) -pad1.SetBottomMargin(0.02) -pad1.SetLogy() # if you want log-y - -histo_DY.SetTitle("") -histo_DY.GetYaxis().SetTitle("Events / 5 GeV") -histo_DY.GetXaxis().SetLabelSize(0) -histo_DY.GetYaxis().SetTitleSize(0.06) -histo_DY.GetYaxis().SetTitleOffset(1.1) -histo_DY.GetYaxis().SetLabelSize(0.05) - -# Draw DY as reference, then data points -histo_DY.SetLineColor(ROOT.kBlue) -histo_DY.SetStats(False) -histo_DY.SetFillColorAlpha(ROOT.kBlue, 0.1) -histo_DY.Draw("HIST") -histo_trueData.SetMarkerColor(ROOT.kBlack) # Change marker color to red -histo_trueData.SetMarkerSize(1.3) # Change marker color to red -histo_trueData.Draw("E1 SAME") - -# CMS label -label = ROOT.TLatex() -label.SetNDC(True) -label.SetTextSize(0.040) -label.DrawLatex(0.12, 0.92, "#bf{CMS} #it{Preliminary}") -label.DrawLatex(0.55, 0.92, "#sqrt{s} = 13.6 TeV, L_{int} = 8.2 fb^{-1}") - -leg = ROOT.TLegend(0.60, 0.70, 0.88, 0.88) -leg.SetBorderSize(0) -leg.SetFillStyle(0) -leg.AddEntry(histo_trueData, "Data", "pe") -leg.AddEntry(histo_DY, "DY", "f") -leg.Draw() - -# Bottom pad: ratio -pad2 = c.cd(2) -pad2.SetPad(0.0, 0.0, 1.0, 0.30) -pad2.SetTopMargin(0.02) -pad2.SetBottomMargin(0.35) - -h_ratio_points = histo_ratio.Clone("h_ratio_points") -h_ratio_points.SetTitle("") -h_ratio_points.SetMarkerStyle(20) -h_ratio_points.SetMarkerSize(0.8) -h_ratio_points.SetLineColor(ROOT.kBlack) -h_ratio_points.SetMarkerColor(ROOT.kBlack) - -h_ratio_points.GetYaxis().SetTitle("Data/DY") -h_ratio_points.GetYaxis().SetNdivisions(505) -h_ratio_points.GetYaxis().SetTitleSize(0.10) -h_ratio_points.GetYaxis().SetTitleOffset(0.45) -h_ratio_points.GetYaxis().SetLabelSize(0.08) -h_ratio_points.GetXaxis().SetTitle("p_{T}^{ll} [GeV]") -h_ratio_points.GetXaxis().SetTitleSize(0.12) -h_ratio_points.GetXaxis().SetLabelSize(0.10) - -h_ratio_points.SetMinimum(0.0) -h_ratio_points.SetMaximum(2.0) - -h_ratio_points.Draw("E1") - -# horizontal line at 1 -line = ROOT.TLine(h_ratio_points.GetXaxis().GetXmin(), 1.0, - h_ratio_points.GetXaxis().GetXmax(), 1.0) -line.SetLineColor(ROOT.kGray+2) -line.SetLineStyle(2) -line.Draw("SAME") - -# --- Fit the ratio with an error function --- -# erf(x; p0, p1, p2, p3) = p0 + p1 * TMath::Erf((x - p2)/p3) -fit_func = ROOT.TF1("fit_erf", - "[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x*x + [6])", - histo_ratio.GetXaxis().GetXmin(), - histo_ratio.GetXaxis().GetXmax()) -fit_func.SetParameters(1.0, 0.05, 10.0, 5.0, 0.0, 0.0, 1.0) # reasonable starting values -fit_func.SetLineColor(ROOT.kRed) - -h_ratio_points.Fit(fit_func, "R") # fit only in the visible x-range -fit_func.Draw("SAME") - -c.SaveAs("ZpTreweighting_main_plus_ratio.pdf") -c.SaveAs("ZpTreweighting_main_plus_ratio.png") - -# --- Optional: save the ratio+fit alone in a separate figure --- -c_ratio_only = ROOT.TCanvas("c_ratio_only", "c_ratio_only", 800, 800) -h_ratio_points.Draw("E1") -h_ratio_points.GetYaxis().SetTitle("Data/DY") -h_ratio_points.GetXaxis().SetTitle("p_{T}^{ll} [GeV]") -line.Draw("SAME") -fit_func.Draw("SAME") -c_ratio_only.SaveAs("ZpTreweighting_ratio_fit.pdf") -c_ratio_only.SaveAs("ZpTreweighting_ratio_fit.png") \ No newline at end of file +fitting_functions = ["[0]*x**6 + [1]*x**5 + [2]*x**4 + [3]*x**3 + [4]*x**2 + [5]*x + [6]", "[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x**2)"] +initial_guesses = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 0.05, 10.0, 5.0, 0.0, 0.0]] +save_name_suffixes = ["poly6", "erf_poly2"] +for fitfunc, initguess, savename in zip(fitting_functions, initial_guesses, save_name_suffixes): + c = ROOT.TCanvas("c", "c", 1000, 1000) + c.Divide(1,2) + + # Top pad: main plot + pad1 = c.cd(1) + pad1.SetPad(0.0, 0.30, 1.0, 1.0) + pad1.SetBottomMargin(0.02) + pad1.SetLogy() # if you want log-y + + histo_DY.SetTitle("") + histo_DY.GetYaxis().SetTitle("Events / 5 GeV") + histo_DY.GetXaxis().SetLabelSize(0) + histo_DY.GetYaxis().SetTitleSize(0.06) + histo_DY.GetYaxis().SetTitleOffset(0.8) + histo_DY.GetYaxis().SetLabelSize(0.05) + histo_DY.GetXaxis().SetRangeUser(0, 80) + histo_DY.SetBins(320) + # Draw DY as reference, then data points + # histo_DY.SetLineColor(ROOT.kBlue) + histo_DY.SetStats(False) + histo_DY.SetFillStyle(3344) + histo_DY.SetFillColorAlpha(ROOT.kBlue, 0.1) + histo_DY.Draw("HIST") + histo_trueData.SetMarkerStyle(8) + histo_trueData.SetMarkerColor(ROOT.kBlack) + histo_trueData.SetMarkerSize(0.8) + histo_trueData.Draw("E1 SAME") + + # CMS label + label = ROOT.TLatex() + label.SetNDC(True) + label.SetTextSize(0.040) + label.DrawLatex(0.12, 0.92, "#bf{CMS} #it{Preliminary}") + label.DrawLatex(0.55, 0.92, "L = 8.2 fb^{-1} (#sqrt{s} = 13.6 TeV)") + + leg = ROOT.TLegend(0.60, 0.70, 0.88, 0.88) + leg.SetBorderSize(0) + leg.SetFillStyle(0) + leg.AddEntry(histo_trueData, "Data - BG", "pe") + leg.AddEntry(histo_DY, "DY", "f") + leg.Draw() + + # Bottom pad: ratio + pad2 = c.cd(2) + pad2.SetPad(0.0, 0.0, 1.0, 0.30) + pad2.SetTopMargin(0.02) + pad2.SetBottomMargin(0.35) + + h_ratio_points = histo_ratio.Clone("h_ratio_points") + h_ratio_points.SetTitle("") + h_ratio_points.SetMarkerStyle(20) + h_ratio_points.SetMarkerSize(0.8) + h_ratio_points.SetLineColor(ROOT.kBlack) + h_ratio_points.SetMarkerColor(ROOT.kBlack) + + h_ratio_points.GetYaxis().SetTitle("(Data-BG)/DY") + h_ratio_points.GetYaxis().SetNdivisions(505) + h_ratio_points.GetYaxis().SetTitleSize(0.12) + h_ratio_points.GetYaxis().SetTitleOffset(0.30) + h_ratio_points.GetYaxis().SetLabelSize(0.08) + h_ratio_points.GetXaxis().SetTitle("p_{T}^{ll} [GeV]") + h_ratio_points.GetXaxis().SetTitleSize(0.12) + h_ratio_points.GetXaxis().SetLabelSize(0.10) + + h_ratio_points.SetMinimum(0.0) + h_ratio_points.SetMaximum(2.0) + + h_ratio_points.Draw("E1") + h_ratio_points.SetStats(False) + + # horizontal line at 1 + line = ROOT.TLine(h_ratio_points.GetXaxis().GetXmin(), 1.0, + h_ratio_points.GetXaxis().GetXmax(), 1.0) + line.SetLineColor(ROOT.kGray+2) + line.SetLineStyle(2) + line.Draw("SAME") + + # --- Fit the ratio with an error function --- + # erf(x; p0, p1, p2, p3) = p0 + p1 * TMath::Erf((x - p2)/p3) + fit_func = ROOT.TF1("fit_erf", + fitfunc, + histo_ratio.GetXaxis().GetXmin(), + 80)#histo_ratio.GetXaxis().GetXmax()) + fit_func.SetParameters(*initguess) # reasonable starting values + fit_func.SetLineColor(ROOT.kRed) + + fit_result = h_ratio_points.Fit(fit_func, "RVS") # fit only in the visible x-range + if fit_result and fit_result.IsValid(): + chi2 = fit_result.Chi2() + ndf = fit_result.Ndf() + chi2_ndf = chi2 / ndf if ndf != 0 else float('inf') + print(f"Chi2/NDF: {chi2_ndf:.3f}") + else: + print("Fit failed or invalid - cannot compute Chi2/NDF") + latex = ROOT.TLatex() + latex.SetNDC(True) + latex.SetTextSize(0.08) + latex.SetTextColor(ROOT.kBlack) + latex.DrawLatex(0.15, 0.55, f"#chi^{{2}}/ndf = {chi2_ndf:.2f}") + func_formula = fit_func.GetTitle() # or fit_func.GetExpFormula() for TF1 + param_values = [fit_func.GetParameter(i) for i in range(fit_func.GetNpar())] + param_str = ", ".join([f"p{i}={v:.2f}" for i, v in enumerate(param_values)]) + # Display fit function and parameters + # latex.DrawLatex(0.15, 0.65, f"f(x) = {func_formula}") + # latex.DrawLatex(0.15, 0.75, param_str) + + fit_func.Draw("SAME") + fit_func.Print("V") + c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.pdf") + c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.png") + + # Commented out as plot quality is very bad + c_ratio_only = ROOT.TCanvas("c_ratio_only", "c_ratio_only", 800, 800) + h_ratio_points.Draw("E1") + h_ratio_points.GetYaxis().SetTitle("(Data-BG)/DY") + h_ratio_points.GetYaxis().SetTitleSize(0.05) + h_ratio_points.GetYaxis().SetTitleOffset(0.8) + h_ratio_points.GetYaxis().SetLabelSize(0.04) + h_ratio_points.GetXaxis().SetTitle("p_{T}^{ll} [GeV]") + h_ratio_points.GetXaxis().SetTitleSize(0.05) + h_ratio_points.GetXaxis().SetLabelSize(0.03) + line.Draw("SAME") + fit_func.Draw("SAME") + c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.pdf") + c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.png") \ No newline at end of file diff --git a/ZpTreweighting/samples.py b/ZpTreweighting/samples.py index 7d0c532d..e089856d 100644 --- a/ZpTreweighting/samples.py +++ b/ZpTreweighting/samples.py @@ -15,10 +15,10 @@ dataReco = 'Run2022_ReReco_nAODv12_Full2022v12' dataSteps = 'DATAl2loose2022v12__sblancof__l2loose' elif dataset_samples == 'amassiro': - mcProduction = 'Summer22_130x_nAODv12_Full2022v12_OLD' + mcProduction = 'Summer22_130x_nAODv12_Full2022v12' mcSteps = 'MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight' # Using DYto2L-2Jets_MLL-50 from Amassiro (DS, 21Nov25) - dataReco = 'Run2022_ReReco_nAODv12_Full2022v12_OLD' - dataSteps = 'DATAl2loose2022v12__l2tight' + dataReco = 'Run2022_ReReco_nAODv12_Full2022v12' + dataSteps = 'DATAl2loose2022v12__l2loose' # fakeSteps = 'DATAl1loose2022EFGv12__fakeW' @@ -148,7 +148,14 @@ def addSampleWeight(samples, sampleName, sampleNameType, weight): higgs_samples = ['GluGluHToWWTo2L2Nu_M125', 'VBFHToWWTo2L2Nu_M125'] samples['top'] = { - 'name': nanoGetSampleFiles(mcDirectory, 'TTTo2L2Nu') + \ + 'name': + nanoGetSampleFiles(mcDirectory, 'ST_t-channel_top') + \ + nanoGetSampleFiles(mcDirectory, 'ST_t-channel_antitop') + \ + nanoGetSampleFiles(mcDirectory, 'ST_s-channel_plus') + \ + nanoGetSampleFiles(mcDirectory, 'ST_s-channel_minus') + \ + nanoGetSampleFiles(mcDirectory, 'ST_tW_top') + \ + nanoGetSampleFiles(mcDirectory, 'ST_tW_antitop') + \ + nanoGetSampleFiles(mcDirectory, 'TTTo2L2Nu') + \ nanoGetSampleFiles(mcDirectory, 'TWminusto2L2Nu') + \ nanoGetSampleFiles(mcDirectory, 'TbarWplusto2L2Nu'), 'weight': mcCommonWeight, From cc29c5ecffd43c9314e378b0df859f6fa2928853 Mon Sep 17 00:00:00 2001 From: CodexForster Date: Sun, 15 Feb 2026 23:15:24 +0530 Subject: [PATCH 08/13] ZpTRW update + accounts for latinos/PlotsConfigurationsRun3 changes --- ZpTreweighting/aliases.py | 210 ++++++++++++------ ZpTreweighting/dyZpTrw.py | 5 + ZpTreweighting/extract_Zptrw.py | 97 ++++---- ZpTreweighting/readme.md | 4 +- ZpTreweighting/samples.py | 4 +- .../{genptll_vs_ptll.cc => twoDhists.cc} | 74 +++++- ZpTreweighting/variables.py | 2 +- 7 files changed, 276 insertions(+), 120 deletions(-) create mode 100644 ZpTreweighting/dyZpTrw.py rename ZpTreweighting/{genptll_vs_ptll.cc => twoDhists.cc} (72%) diff --git a/ZpTreweighting/aliases.py b/ZpTreweighting/aliases.py index e56d90e0..3707f2bc 100644 --- a/ZpTreweighting/aliases.py +++ b/ZpTreweighting/aliases.py @@ -11,9 +11,9 @@ # Commented out as not used (DS, 19Nov25) # mc_emb = [skey for skey in samples if skey not in ('Fake', 'DATA', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] -# LepCut2l__ele_wp90iso__mu_cut_TightID_POG -eleWP = 'mvaWinter22V2Iso_WP90' -muWP = 'cut_Tight_HWW' +# Using LepSF2l__ele_cutBased_LooseID_tthMVA_Run3__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 from latest git repo push (https://github.com/latinos/PlotsConfigurationsRun3/blob/f8a0f50dfe6301543203d9d260ad721204a2739f/ControlRegions/DY/2022_v12/aliases.py#L14-L15) +eleWP = 'cutBased_LooseID_tthMVA_Run3' +muWP = 'cut_TightID_pfIsoTight_HWW_tthmva_67' aliases['LepWPCut'] = { 'expr': 'LepCut2l__ele_'+eleWP+'__mu_'+muWP, @@ -33,7 +33,81 @@ aliases['gen_Zpt'] = { # 'linesToAdd': [".L /afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+"], - 'linesToAdd': ['.L /eos/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+'], + # 'linesToAdd': ['.L /eos/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+'], + 'linesToAdd': [ + """ +#ifndef getGenZpt +#define getGenZpt + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" + +using namespace ROOT; +using namespace ROOT::VecOps; + +double GetGenZpt( + int nGenPart, + RVecF GenPart_pt, + RVecI GenPart_pdgId, + RVecI GenPart_genPartIdxMother, + RVecI GenPart_statusFlags, + float gen_ptll + ){ + + + + // Find Gen pT of Z decaying into leptons + unsigned nGen = nGenPart; + std::vector LepCands{}; + std::vector MotherIdx{}; + std::vector MotherPdgId{}; + int pdgId, sFlag, MIdx; + bool hasZ = false; + //std::cout << "==========" << std::endl; + for (unsigned iGen{0}; iGen != nGen; ++iGen){ + pdgId = std::abs(GenPart_pdgId[iGen]); + sFlag = GenPart_statusFlags[iGen]; + //std::cout << pdgId << " ; " << sFlag << " ; " << GenPart_pt->At(iGen) << " ; " << GenPart_genPartIdxMother->At(iGen) << std::endl; + if (((pdgId == 11) || (pdgId == 13) || (pdgId == 15)) && ((sFlag >> 0 & 1) || (sFlag >> 2 & 1) || (sFlag >> 3 & 1) || (sFlag >> 4 & 1))){ + LepCands.push_back(iGen); + MIdx = GenPart_genPartIdxMother[iGen]; + MotherIdx.push_back(MIdx); + if (MIdx > -1){ + MotherPdgId.push_back(GenPart_pdgId[MIdx]); + if (GenPart_pdgId[MIdx]==23) hasZ = true; + }else{ + MotherPdgId.push_back(0); + } + } + } + + //std::cout << "Check:" << std::endl; + for (unsigned iGen{0}; iGen != LepCands.size(); ++iGen){ + for (unsigned jGen{0}; jGen != LepCands.size(); ++jGen){ + if (jGen <= iGen) continue; + //std::cout << iGen << " ; " << MotherIdx[iGen] << " ; " << jGen << " ; " << MotherIdx[jGen] << " ; " << MotherPdgId[iGen] << " ; " << hasZ << std::endl; + // Some DY samples generate the Z; others have the two leptons produced directly -> motherId is 0 for those events + if (hasZ){ + if (MotherIdx[iGen] == MotherIdx[jGen] && MotherPdgId[iGen] == 23) return GenPart_pt[MotherIdx[iGen]]; + }else{ + if (MotherIdx[iGen] == MotherIdx[jGen] && MotherIdx[iGen] == 0) return GenPart_pt[MotherIdx[iGen]]; + } + } + } + //std::cout << "Falling back!" << std::endl; + return gen_ptll; + +} + +#endif + """], 'class': 'GetGenZpt', 'args': 'nGenPart, GenPart_pt, GenPart_pdgId, GenPart_genPartIdxMother, GenPart_statusFlags, gen_ptll', # 'expr': 'gen_ptll', @@ -60,6 +134,12 @@ 'expr' : 'Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', } +exec(open('dyZpTrw.py', "r").read()) +aliases['DY_LO_ZpTrw'] = { + 'expr': '('+DYrew['2022']['LO'].replace('x', 'gen_Zpt')+')*(zeroJet)*(ptll < 50) + 1*(zeroJet)*(ptll >= 50)', + 'samples': ['DY'] +} + ######################################################################## # B-Tagging WP: https://btv-wiki.docs.cern.ch/ScaleFactors/Run3Summer22/ ######################################################################## @@ -97,7 +177,7 @@ } # Algorithm and WP selection -bAlgo = 'DeepFlavB' # ['DeepFlavB','RobustParTAK4B','PNetB'] +bAlgo = 'PNetB' # ['DeepFlavB','RobustParTAK4B','PNetB'] WP = 'loose' # ['loose','medium','tight','xtight','xxtight'] # Access information from dictionaries @@ -105,76 +185,72 @@ bSF = btagging_SFs[bAlgo] # # B tagging selections and scale factors -if dataset_samples == 'amassiro': - aliases['bVeto'] = { - 'expr': f'Sum(CleanJet_pt > 20. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) == 0' - } - - aliases['bVetoSF'] = { - 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>20 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<20 || abs(CleanJet_eta)>2.5))))'.format(bSF), - 'samples': mc - } - - aliases['bReq'] = { - 'expr': f'Sum(CleanJet_pt > 30. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) >= 1' - } - - aliases['bReqSF'] = { - 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>30 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<30 || abs(CleanJet_eta)>2.5))))'.format(bSF), - 'samples': mc - } - - # Top control region - aliases['topcr'] = { - 'expr': 'mtw2>30 && mll>50 && ((zeroJet && !bVeto) || bReq)' - } - - # WW control region - aliases['wwcr'] = { - 'expr': 'mth>60 && mtw2>30 && mll>100 && bVeto' - } - - # Overall b tag SF - aliases['btagSF'] = { - 'expr': '(bVeto || (topcr && zeroJet))*bVetoSF + (topcr && !zeroJet)*bReqSF', - 'samples': mc - } - - # Systematic uncertainty variations - for shift in ['jes','lf','hf','lfstats1','lfstats2','hfstats1','hfstats2','cferr1','cferr2']: - - for targ in ['bVeto', 'bReq']: - alias = aliases['%sSF%sup' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) - alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_up_%s' % shift) - - alias = aliases['%sSF%sdown' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) - alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_down_%s' % shift) +aliases['bVeto'] = { + 'expr': f'Sum(CleanJet_pt > 20. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) == 0' +} - aliases['btagSF%sup' % shift] = { - 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'up'), - 'samples': mc - } +aliases['bReq'] = { + 'expr': f'Sum(CleanJet_pt > 30. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) >= 1' +} - aliases['btagSF%sdown' % shift] = { - 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'down'), - 'samples': mc - } +# Commenting out, as this was not included in latest git repo (https://github.com/latinos/PlotsConfigurationsRun3/blob/f8a0f50dfe6301543203d9d260ad721204a2739f/ControlRegions/DY/2022_v12/aliases.py#L89-L104) +# aliases['bVetoSF'] = { +# 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>20 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<20 || abs(CleanJet_eta)>2.5))))'.format(bSF), +# 'samples': mc +# } + +# aliases['bReqSF'] = { +# 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>30 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<30 || abs(CleanJet_eta)>2.5))))'.format(bSF), +# 'samples': mc +# } + +# # Top control region +# aliases['topcr'] = { +# 'expr': 'mtw2>30 && mll>50 && ((zeroJet && !bVeto) || bReq)' +# } + +# # WW control region +# aliases['wwcr'] = { +# 'expr': 'mth>60 && mtw2>30 && mll>100 && bVeto' +# } + +# # Overall b tag SF +# aliases['btagSF'] = { +# 'expr': '(bVeto || (topcr && zeroJet))*bVetoSF + (topcr && !zeroJet)*bReqSF', +# 'samples': mc +# } + +# # Systematic uncertainty variations +# for shift in ['jes','lf','hf','lfstats1','lfstats2','hfstats1','hfstats2','cferr1','cferr2']: + +# for targ in ['bVeto', 'bReq']: +# alias = aliases['%sSF%sup' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) +# alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_up_%s' % shift) + +# alias = aliases['%sSF%sdown' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) +# alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_down_%s' % shift) + +# aliases['btagSF%sup' % shift] = { +# 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'up'), +# 'samples': mc +# } + +# aliases['btagSF%sdown' % shift] = { +# 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'down'), +# 'samples': mc +# } ########################################################################## # End of b tagging ########################################################################## # Data/MC scale factors and systematic uncertainties -if dataset_samples == 'amassiro': - aliases['SFweight'] = { - 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF','btagSF']), - 'samples': mc - } -elif dataset_samples == 'calderon': - aliases['SFweight'] = { - 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF','1']), # Remove btagSF for samples from Calderon (DS, 22Nov25) - 'samples': mc - } +aliases['SFweight'] = { + # 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF','btagSF']), + 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF']), + 'samples': mc +} + aliases['SFweightEleUp'] = { 'expr': 'LepSF2l__ele_'+eleWP+'__Up', 'samples': mc diff --git a/ZpTreweighting/dyZpTrw.py b/ZpTreweighting/dyZpTrw.py new file mode 100644 index 00000000..de10245b --- /dev/null +++ b/ZpTreweighting/dyZpTrw.py @@ -0,0 +1,5 @@ +DYrew={ + '2022': { + 'LO': "0.949213*(0.187030*TMath::Erf((x-9.106432)/3.612284)-0.021800*x+0.000354*TMath::Sq(x)+1.209758)" + } +} \ No newline at end of file diff --git a/ZpTreweighting/extract_Zptrw.py b/ZpTreweighting/extract_Zptrw.py index 4084c74d..15404a1c 100644 --- a/ZpTreweighting/extract_Zptrw.py +++ b/ZpTreweighting/extract_Zptrw.py @@ -6,13 +6,19 @@ import matplotlib.pyplot as plt import numpy as np from ROOT import TFitResultPtr +import argparse # CMS plot style hep.style.use("CMS") style = hep.style.CMS style["font.size"] = 18 plt.style.use(style) -root_file = ROOT.TFile("mkShapes__ZpTreweighting.root") +parser = argparse.ArgumentParser(description='Extract data and fit with Gaussian.') +parser.add_argument('-f', action='store_true', help='Fit the ratio plot using Erf.') +args = parser.parse_args() + +# root_file = ROOT.TFile("mkShapes__ZpTreweighting.root") +root_file = ROOT.TFile("mkShapes__beforeZpTreweighting_highLepPtThreshold_30_18.root") zee_dir = root_file.Get("Zmm_0j") ptll_dir = zee_dir.Get("ptll") @@ -32,9 +38,10 @@ histo_ratio = histo_trueData.Clone("histo_ratio") histo_ratio.Divide(histo_DY) -fitting_functions = ["[0]*x**6 + [1]*x**5 + [2]*x**4 + [3]*x**3 + [4]*x**2 + [5]*x + [6]", "[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x**2)"] -initial_guesses = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 0.05, 10.0, 5.0, 0.0, 0.0]] -save_name_suffixes = ["poly6", "erf_poly2"] +# fitting_functions = ["[0]*x**6 + [1]*x**5 + [2]*x**4 + [3]*x**3 + [4]*x**2 + [5]*x + [6]", "[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x**2)"] +fitting_functions = ["[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x**2 + [6])"] +initial_guesses = [[1.0, 0.0, 5.0, 10.0, 0.0, 0.0, 1.0]] +save_name_suffixes = ["erf_poly2"] for fitfunc, initguess, savename in zip(fitting_functions, initial_guesses, save_name_suffixes): c = ROOT.TCanvas("c", "c", 1000, 1000) c.Divide(1,2) @@ -52,7 +59,6 @@ histo_DY.GetYaxis().SetTitleOffset(0.8) histo_DY.GetYaxis().SetLabelSize(0.05) histo_DY.GetXaxis().SetRangeUser(0, 80) - histo_DY.SetBins(320) # Draw DY as reference, then data points # histo_DY.SetLineColor(ROOT.kBlue) histo_DY.SetStats(False) @@ -62,6 +68,7 @@ histo_trueData.SetMarkerStyle(8) histo_trueData.SetMarkerColor(ROOT.kBlack) histo_trueData.SetMarkerSize(0.8) + histo_trueData.GetXaxis().SetRangeUser(0, 80) histo_trueData.Draw("E1 SAME") # CMS label @@ -99,6 +106,7 @@ h_ratio_points.GetXaxis().SetTitle("p_{T}^{ll} [GeV]") h_ratio_points.GetXaxis().SetTitleSize(0.12) h_ratio_points.GetXaxis().SetLabelSize(0.10) + h_ratio_points.GetXaxis().SetRangeUser(0, 80) h_ratio_points.SetMinimum(0.0) h_ratio_points.SetMaximum(2.0) @@ -112,38 +120,38 @@ line.SetLineColor(ROOT.kGray+2) line.SetLineStyle(2) line.Draw("SAME") - - # --- Fit the ratio with an error function --- - # erf(x; p0, p1, p2, p3) = p0 + p1 * TMath::Erf((x - p2)/p3) - fit_func = ROOT.TF1("fit_erf", - fitfunc, - histo_ratio.GetXaxis().GetXmin(), - 80)#histo_ratio.GetXaxis().GetXmax()) - fit_func.SetParameters(*initguess) # reasonable starting values - fit_func.SetLineColor(ROOT.kRed) - - fit_result = h_ratio_points.Fit(fit_func, "RVS") # fit only in the visible x-range - if fit_result and fit_result.IsValid(): - chi2 = fit_result.Chi2() - ndf = fit_result.Ndf() - chi2_ndf = chi2 / ndf if ndf != 0 else float('inf') - print(f"Chi2/NDF: {chi2_ndf:.3f}") - else: - print("Fit failed or invalid - cannot compute Chi2/NDF") - latex = ROOT.TLatex() - latex.SetNDC(True) - latex.SetTextSize(0.08) - latex.SetTextColor(ROOT.kBlack) - latex.DrawLatex(0.15, 0.55, f"#chi^{{2}}/ndf = {chi2_ndf:.2f}") - func_formula = fit_func.GetTitle() # or fit_func.GetExpFormula() for TF1 - param_values = [fit_func.GetParameter(i) for i in range(fit_func.GetNpar())] - param_str = ", ".join([f"p{i}={v:.2f}" for i, v in enumerate(param_values)]) - # Display fit function and parameters - # latex.DrawLatex(0.15, 0.65, f"f(x) = {func_formula}") - # latex.DrawLatex(0.15, 0.75, param_str) - - fit_func.Draw("SAME") - fit_func.Print("V") + if args.f: + # --- Fit the ratio with an error function --- + # erf(x; p0, p1, p2, p3) = p0 + p1 * TMath::Erf((x - p2)/p3) + fit_func = ROOT.TF1("fit_erf", + fitfunc, + histo_ratio.GetXaxis().GetXmin(), + 80)#histo_ratio.GetXaxis().GetXmax()) + fit_func.SetParameters(*initguess) # reasonable starting values + fit_func.SetLineColor(ROOT.kRed) + + fit_result = h_ratio_points.Fit(fit_func, "RVS") # fit only in the visible x-range + if fit_result and fit_result.IsValid(): + chi2 = fit_result.Chi2() + ndf = fit_result.Ndf() + chi2_ndf = chi2 / ndf if ndf != 0 else float('inf') + print(f" ============ Chi2/NDF: {chi2_ndf:.3f} ============") + latex = ROOT.TLatex() + latex.SetNDC(True) + latex.SetTextSize(0.08) + latex.SetTextColor(ROOT.kBlack) + latex.DrawLatex(0.15, 0.55, f"#chi^{{2}}/ndf = {chi2_ndf:.2f}") + func_formula = fit_func.GetTitle() # or fit_func.GetExpFormula() for TF1 + param_values = [fit_func.GetParameter(i) for i in range(fit_func.GetNpar())] + param_str = ", ".join([f"p{i}={v:.2f}" for i, v in enumerate(param_values)]) + # # Display fit function and parameters + # latex.DrawLatex(0.15, 0.65, f"f(x) = {func_formula}") + # latex.DrawLatex(0.15, 0.75, param_str) + else: + print("Fit failed or invalid - cannot compute Chi2/NDF") + + fit_func.Draw("SAME") + fit_func.Print("V") c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.pdf") c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.png") @@ -158,6 +166,19 @@ h_ratio_points.GetXaxis().SetTitleSize(0.05) h_ratio_points.GetXaxis().SetLabelSize(0.03) line.Draw("SAME") - fit_func.Draw("SAME") + if args.f: + fit_func.Draw("SAME") + if fit_result and fit_result.IsValid(): + latex = ROOT.TLatex() + latex.SetNDC(True) + latex.SetTextSize(0.025) + latex.SetTextColor(ROOT.kBlack) + latex.DrawLatex(0.15, 0.15, f"#chi^{{2}}/ndf = {chi2_ndf:.2f}") + func_formula = fit_func.GetTitle() # or fit_func.GetExpFormula() for TF1 + param_values = [fit_func.GetParameter(i) for i in range(fit_func.GetNpar())] + param_str = ", ".join([f"p{i}={v:.2f}" for i, v in enumerate(param_values)]) + # Display fit function and parameters + latex.DrawLatex(0.15, 0.25, param_str) + latex.DrawLatex(0.15, 0.35, f"f(x) = {func_formula}") c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.pdf") c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.png") \ No newline at end of file diff --git a/ZpTreweighting/readme.md b/ZpTreweighting/readme.md index 0168e9f9..62d25ecd 100644 --- a/ZpTreweighting/readme.md +++ b/ZpTreweighting/readme.md @@ -1,6 +1,6 @@ -In order to produce the 2D histograms looking at gen_pTll vs pTll, run the following from the ZpTreweighting/ folder: +In order to produce the 2D histograms looking at gen_pTll vs pTll, an example runline(s) from the ZpTreweighting/ folder are as follows: ''' root -.x genptll_vs_ptll.cc(2022, "DY", "DeepFlavB", "loose") +.x twoDhists.cc(2022, "DY", "DeepFlavB", "loose") ''' The arguments (2022, "DY", "DeepFlavB", "loose") are the default ones, so those could be omitted. \ No newline at end of file diff --git a/ZpTreweighting/samples.py b/ZpTreweighting/samples.py index e089856d..8d6d9da4 100644 --- a/ZpTreweighting/samples.py +++ b/ZpTreweighting/samples.py @@ -18,7 +18,7 @@ mcProduction = 'Summer22_130x_nAODv12_Full2022v12' mcSteps = 'MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight' # Using DYto2L-2Jets_MLL-50 from Amassiro (DS, 21Nov25) dataReco = 'Run2022_ReReco_nAODv12_Full2022v12' - dataSteps = 'DATAl2loose2022v12__l2loose' + dataSteps = 'DATAl2loose2022v12__l2loose' # Choose l2loose sample but apply tight selections in analysis (eleWP and muWP) # fakeSteps = 'DATAl1loose2022EFGv12__fakeW' @@ -142,6 +142,8 @@ def addSampleWeight(samples, sampleName, sampleNameType, weight): 'FilesPerJob': 2, } +addSampleWeight(samples,'DY','DYto2L-2Jets_MLL-50','DY_LO_ZpTrw') + # remove backgrounds from data for ZpT reweighting: top_samples = ['TTTo2L2Nu', 'TWminusto2L2Nu', 'TbarWplusto2L2Nu']#, 'ST_tW_top'] diboson_samples = ['WWTo2L2Nu', 'WZTo3LNu', 'GluGlutoContintoWWtoENuENu', 'GluGlutoContintoWWtoENuMuNu', 'GluGlutoContintoWWtoENuTauNu', 'GluGlutoContintoWWtoMuNuENu', 'GluGlutoContintoWWtoMuNuMuNu', 'GluGlutoContintoWWtoMuNuTauNu', 'GluGlutoContintoWWtoTauNuENu', 'GluGlutoContintoWWtoTauNuMuNu', 'GluGlutoContintoWWtoTauNuTauNu', 'WGtoLNuG-1J_PTG10to100', 'WGtoLNuG-1J_PTG100to200', 'WGtoLNuG-1J_PTG200to400', 'WGtoLNuG-1J_PTG400to600', 'WGtoLNuG-1J_PTG600'] diff --git a/ZpTreweighting/genptll_vs_ptll.cc b/ZpTreweighting/twoDhists.cc similarity index 72% rename from ZpTreweighting/genptll_vs_ptll.cc rename to ZpTreweighting/twoDhists.cc index 37fd995c..04d98d4d 100644 --- a/ZpTreweighting/genptll_vs_ptll.cc +++ b/ZpTreweighting/twoDhists.cc @@ -30,6 +30,9 @@ #include #include +#include "ROOT/RVec.hxx" +using namespace ROOT; +using namespace ROOT::VecOps; struct dataset { int year; @@ -43,7 +46,10 @@ dataset mkDataset(int year) { d.year = year; if (year == 2022) { d.samples = { - {"DY" , "/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Summer22_130x_nAODv12_Full2022v12_OLD/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/nanoLatino_DYto2L-2Jets_MLL-50__part*.root"} + {"DY" , "/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/nanoLatino_DYto2L-2Jets_MLL-50__part*.root"} + // The DYto2L-2Jets_MLL-50 sample contains in input cards: + // set run_card ptj 10 (minimum jet pT = 10 GeV) + // set run_card mll_sf 50.0 (minimum ll invariant mass = 50 GeV) }; d.algo = { {"DeepFlavB", {{"loose" , 0.0583}, {"medium" , 0.3086}, {"tight" , 0.7183}}}, @@ -63,11 +69,18 @@ dataset mkDataset(int year) { return d; } +int CountJetsAbovePt(float* pts, Int_t njet, float threshold) { + int count = 0; + for (Int_t i = 0; i < njet; ++i) { + if (pts[i] > threshold) ++count; + } + return count; +} -void genptll_vs_ptll( +void twoDhists( int year = 2022, std::string process = "DY", - std::string algo = "DeepFlavB", + std::string algo = "PNetB", std::string const WP = "loose" ){ dataset d = mkDataset(year); @@ -86,6 +99,9 @@ void genptll_vs_ptll( Events->SetBranchStatus("*", 0); Events->SetBranchStatus("XSWeight", 1); Events->SetBranchStatus("nCleanJet", 1); + Events->SetBranchStatus("CleanJet_pt", 1); + Events->SetBranchStatus("nGenJet", 1); + Events->SetBranchStatus("GenJet_pt", 1); Events->SetBranchStatus("nLepton", 1); Events->SetBranchStatus("Lepton_pt", 1); Events->SetBranchStatus("Lepton_eta", 1); @@ -96,6 +112,12 @@ void genptll_vs_ptll( double XSWeight; Int_t nCleanJet; + // ROOT::RVec CleanJet_pt; // or using RVecF CleanJet_pt; + // std::vector* CleanJet_pt = nullptr; + float CleanJet_pt[200]; + float GenJet_pt[200]; + + Int_t nGenJet; Int_t nLepton; float Lepton_pt[100]; float Lepton_eta[100]; @@ -105,6 +127,9 @@ void genptll_vs_ptll( Events->SetBranchAddress("XSWeight", &XSWeight); Events->SetBranchAddress("nCleanJet", &nCleanJet); + Events->SetBranchAddress("CleanJet_pt", CleanJet_pt); + Events->SetBranchAddress("nGenJet", &nGenJet); + Events->SetBranchAddress("GenJet_pt", &GenJet_pt); Events->SetBranchAddress("nLepton", &nLepton); Events->SetBranchAddress("Lepton_pt", Lepton_pt); Events->SetBranchAddress("Lepton_eta", Lepton_eta); @@ -121,10 +146,14 @@ void genptll_vs_ptll( */ Float_t ptbins[11] = {0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100}; + Float_t jetbins[11] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}; TH1F *ptll_pull = new TH1F{"ptll_pull", "ptll_pull", 100, -2, 2}; ptll_pull->GetXaxis()->SetTitle("(reco ptll - gen ptll)/gen ptll"); + TH1F *gen_jet_pt = new TH1F{"gen_jet_pt", "gen_jet_pt", 50, 0, 100}; + gen_jet_pt->GetXaxis()->SetTitle("pT [GeV]"); + TH2F *genReco_pT_0j = new TH2F{"gen_vs_reco_pt_0j", "gen_vs_reco_pt_0j", 10, ptbins, 10, ptbins}; genReco_pT_0j->GetYaxis()->SetTitle("p_{T} [GeV]"); genReco_pT_0j->GetXaxis()->SetTitle("gen p_{T} [GeV]"); @@ -144,6 +173,10 @@ void genptll_vs_ptll( TH2F *genReco_pT_inclJets = new TH2F{"gen_vs_reco_pt_inclJets", "gen_vs_reco_pt_inclJets", 10, ptbins, 10, ptbins}; genReco_pT_inclJets->GetYaxis()->SetTitle("p_{T} [GeV]"); genReco_pT_inclJets->GetXaxis()->SetTitle("gen p_{T} [GeV]"); + + TH2F *gen_vs_reco_nJet = new TH2F{"gen_vs_reco_nJet", "gen_vs_reco_nJet", 10, jetbins, 10, jetbins}; + gen_vs_reco_nJet->GetYaxis()->SetTitle("Reco nJet"); + gen_vs_reco_nJet->GetXaxis()->SetTitle("Gen nJet"); int entries = Events->GetEntries(); for (unsigned int i = 0; i < Events->GetEntries(); i ++) @@ -168,13 +201,23 @@ void genptll_vs_ptll( {genReco_pT_2j->Fill(gen_ptll, ptll, XSWeight);} else if (nCleanJet >= 3) {genReco_pT_3pj->Fill(gen_ptll, ptll, XSWeight);} + + int nRecoJet = CountJetsAbovePt(CleanJet_pt, nCleanJet, 30.); + int nCleanGenJet = CountJetsAbovePt(GenJet_pt, nGenJet, 5.); + gen_vs_reco_nJet->Fill(nCleanGenJet, nRecoJet, XSWeight); // Sum(CleanJet_pt>30) = nRecoJet + // gen_vs_reco_nJet->Fill(nGenJet, Sum(CleanJet_pt>30), XSWeight); + + for (int gj = 0; gj < nGenJet; gj++) + {gen_jet_pt->Fill(GenJet_pt[gj]);} } ptll_pull->Write(); + gen_jet_pt->Write(); genReco_pT_inclJets->Write(); genReco_pT_0j->Write(); genReco_pT_1j->Write(); genReco_pT_2j->Write(); genReco_pT_3pj->Write(); + gen_vs_reco_nJet->Write(); auto normalize_columns = [](TH2F *h) { int nx = h->GetNbinsX(); @@ -193,7 +236,7 @@ void genptll_vs_ptll( } }; - auto draw_and_save = [&](TH2F *h, const char *tag) { + auto draw_and_save = [&](TH2F *h, const char *qty, const char *tag) { normalize_columns(h); // gStyle->SetOptStat(0); TCanvas *c = new TCanvas(Form("c_%s",tag), "",800,600); @@ -210,8 +253,8 @@ void genptll_vs_ptll( st->Draw(); } gPad->Update(); // Ensure stat box is created - TString cname = Form("genReco_pT2Dhist_%s_%d_%s_%s_%s.png", - tag, year, process.c_str(), algo.c_str(), WP.c_str()); + TString cname = Form("genReco_%s2Dhist_%s_%d_%s_%s_%s.png", + qty, tag, year, process.c_str(), algo.c_str(), WP.c_str()); c->SaveAs(cname); delete c; }; @@ -224,11 +267,20 @@ void genptll_vs_ptll( c1->SaveAs(cname1); delete c1; - draw_and_save(genReco_pT_inclJets, "inclJets"); - draw_and_save(genReco_pT_0j, "0j"); - draw_and_save(genReco_pT_1j, "1j"); - draw_and_save(genReco_pT_2j, "2j"); - draw_and_save(genReco_pT_3pj,"3pj"); + TCanvas *c2 = new TCanvas("c2", "",800,600); + c2->cd(); + gen_jet_pt->Draw(); + // gen_jet_pt->Fit("gaus"); // Fit with Gaussian + TString cname2 = "gen_jet_pt" + std::to_string(year) + "_" + process + "_" + algo + "_" + WP + ".png"; + c2->SaveAs(cname2); + delete c2; + + draw_and_save(genReco_pT_inclJets, "pT", "inclJets"); + draw_and_save(genReco_pT_0j, "pT", "0j"); + draw_and_save(genReco_pT_1j, "pT", "1j"); + draw_and_save(genReco_pT_2j, "pT", "2j"); + draw_and_save(genReco_pT_3pj, "pT", "3pj"); + draw_and_save(gen_vs_reco_nJet, "nJet", ""); } diff --git a/ZpTreweighting/variables.py b/ZpTreweighting/variables.py index 94b55bfd..ec35774b 100644 --- a/ZpTreweighting/variables.py +++ b/ZpTreweighting/variables.py @@ -24,7 +24,7 @@ variables['ptll'] = { 'name': 'ptll', - 'range' : (40,0,200), + 'range' : (160,0,80), 'xaxis' : 'p_{T}^{ll} [GeV]', 'fold' : 0 } From 43e8f8f65e58a940051067fce36ae8f31724967e Mon Sep 17 00:00:00 2001 From: CodexForster Date: Wed, 25 Feb 2026 16:26:34 +0530 Subject: [PATCH 09/13] Normalization in ZpTrw and lep subtraction in reco_gen_nJet2Dhist --- ZpTreweighting/dyZpTrw.py | 2 +- ZpTreweighting/extract_Zptrw.py | 42 +++++++++- ZpTreweighting/twoDhists.cc | 136 +++++++++++++++++++++++++------- 3 files changed, 145 insertions(+), 35 deletions(-) diff --git a/ZpTreweighting/dyZpTrw.py b/ZpTreweighting/dyZpTrw.py index de10245b..82f0ea2c 100644 --- a/ZpTreweighting/dyZpTrw.py +++ b/ZpTreweighting/dyZpTrw.py @@ -1,5 +1,5 @@ DYrew={ '2022': { - 'LO': "0.949213*(0.187030*TMath::Erf((x-9.106432)/3.612284)-0.021800*x+0.000354*TMath::Sq(x)+1.209758)" + 'LO': "1.0133492539254507*0.843196*(0.211634*TMath::Erf((x-9.120552)/3.728196)-0.024628*x+0.000400*TMath::Sq(x)+1.361984)" } } \ No newline at end of file diff --git a/ZpTreweighting/extract_Zptrw.py b/ZpTreweighting/extract_Zptrw.py index 15404a1c..2d1d9340 100644 --- a/ZpTreweighting/extract_Zptrw.py +++ b/ZpTreweighting/extract_Zptrw.py @@ -17,8 +17,8 @@ parser.add_argument('-f', action='store_true', help='Fit the ratio plot using Erf.') args = parser.parse_args() -# root_file = ROOT.TFile("mkShapes__ZpTreweighting.root") -root_file = ROOT.TFile("mkShapes__beforeZpTreweighting_highLepPtThreshold_30_18.root") +root_file = ROOT.TFile("mkShapes__ZpTreweighting.root") +# root_file = ROOT.TFile("mkShapes__beforeZpTreweighting_highLepPtThreshold_30_18.root") zee_dir = root_file.Get("Zmm_0j") ptll_dir = zee_dir.Get("ptll") @@ -34,10 +34,32 @@ histo_trueData.Add(histo_diboson, -1) histo_trueData.Add(histo_SMhiggs, -1) +# # Rebin hists +# histo_trueData.Rebin(4) +# histo_DY.Rebin(4) + # Create a ratio plot of DATA to DY histo_ratio = histo_trueData.Clone("histo_ratio") histo_ratio.Divide(histo_DY) +# Calculate the integral/sum of histo_DY and histo_ratio for x axis in [0, 50) +integral_histo_DY = histo_DY.Integral(histo_DY.FindBin(0), histo_DY.FindBin(50) - 1) +integral_histo_ratio = histo_ratio.Integral(histo_ratio.FindBin(0), histo_ratio.FindBin(50) - 1) +numerator = 0.0 +denominator = 0.0 +first_bin = histo_DY.FindBin(0) +last_bin = histo_DY.FindBin(50) + +for bin_idx in range(first_bin, last_bin + 1): + mc_events = histo_DY.GetBinContent(bin_idx) + weight = histo_ratio.GetBinContent(bin_idx) + numerator += mc_events + denominator += mc_events * weight + +norm_factor = numerator / denominator if denominator != 0 else 1.0 +print("Normalization factor:", norm_factor) + + # fitting_functions = ["[0]*x**6 + [1]*x**5 + [2]*x**4 + [3]*x**3 + [4]*x**2 + [5]*x + [6]", "[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x**2)"] fitting_functions = ["[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x**2 + [6])"] initial_guesses = [[1.0, 0.0, 5.0, 10.0, 0.0, 0.0, 1.0]] @@ -178,7 +200,19 @@ param_values = [fit_func.GetParameter(i) for i in range(fit_func.GetNpar())] param_str = ", ".join([f"p{i}={v:.2f}" for i, v in enumerate(param_values)]) # Display fit function and parameters - latex.DrawLatex(0.15, 0.25, param_str) latex.DrawLatex(0.15, 0.35, f"f(x) = {func_formula}") + latex.DrawLatex(0.15, 0.3, param_str) + latex.DrawLatex(0.15, 0.25, f"Normalization factor = {norm_factor:.2f}") + formula = fit_func.GetTitle() # e.g., "[0]*x + [1]" + n_params = fit_func.GetNpar() + params = [fit_func.GetParameter(i) for i in range(n_params)] + # Replace [i] with parameter values + for i, p in enumerate(params): + formula = formula.replace(f"[{i}]", f"{p:.3f}") + print(f"Fit function with parameters: {formula}") + c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.pdf") - c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.png") \ No newline at end of file + c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.png") +print(f"Integral of DY histogram from 0 to 50 GeV: {integral_histo_DY}") +print(f"Integral of ratio histogram from 0 to 50 GeV: {integral_histo_ratio}") +print(f"Normalization factor = {norm_factor}") \ No newline at end of file diff --git a/ZpTreweighting/twoDhists.cc b/ZpTreweighting/twoDhists.cc index 04d98d4d..5a4d3e11 100644 --- a/ZpTreweighting/twoDhists.cc +++ b/ZpTreweighting/twoDhists.cc @@ -69,6 +69,7 @@ dataset mkDataset(int year) { return d; } +// Count the number of jets above a defined threshold (used for counting number of clean jets) int CountJetsAbovePt(float* pts, Int_t njet, float threshold) { int count = 0; for (Int_t i = 0; i < njet; ++i) { @@ -77,6 +78,35 @@ int CountJetsAbovePt(float* pts, Int_t njet, float threshold) { return count; } +// Calculate delta R between two vectors +float deltaR(float GenJet_eta, float GenJet_phi, float Lepton_eta, float Lepton_phi) { + float dEta = GenJet_eta - Lepton_eta; + float dPhi = GenJet_phi - Lepton_phi; + while (dPhi > M_PI) dPhi -= 2 * M_PI; + while (dPhi <= -M_PI) dPhi += 2 * M_PI; + return std::sqrt(dEta * dEta + dPhi * dPhi); +} + +// Count number of clean jets (pT above a defined threshold) not matched to any lepton +int CountJetsAbovePtNoLeps(float* pts, Int_t njet, float threshold, Int_t nLepton, float* Lepton_phi, float* Lepton_eta, float* GenJet_phi, float* GenJet_eta) { + int count = 0; + for (Int_t i = 0; i < njet; ++i) { + if (pts[i] > threshold) { + bool matched = false; + for (Int_t l = 0; l < nLepton; ++l) { + if (deltaR(GenJet_eta[i], GenJet_phi[i], Lepton_eta[l], Lepton_phi[l]) < 0.4) { + matched = true; + break; + } + } + if (!matched) { + ++count; + } + } + } + return count; +} + void twoDhists( int year = 2022, std::string process = "DY", @@ -98,45 +128,74 @@ void twoDhists( Events->SetBranchStatus("*", 0); Events->SetBranchStatus("XSWeight", 1); - Events->SetBranchStatus("nCleanJet", 1); - Events->SetBranchStatus("CleanJet_pt", 1); + // Gen variables Events->SetBranchStatus("nGenJet", 1); Events->SetBranchStatus("GenJet_pt", 1); + Events->SetBranchStatus("GenJet_eta", 1); + Events->SetBranchStatus("GenJet_phi", 1); + Events->SetBranchStatus("nLeptonGen", 1); + Events->SetBranchStatus("LeptonGen_pt", 1); + Events->SetBranchStatus("LeptonGen_eta", 1); + Events->SetBranchStatus("LeptonGen_phi", 1); + Events->SetBranchStatus("gen_ptll", 1); + // Reco variables + Events->SetBranchStatus("nCleanJet", 1); + Events->SetBranchStatus("CleanJet_pt", 1); + Events->SetBranchStatus("CleanJet_phi", 1); + Events->SetBranchStatus("CleanJet_eta", 1); Events->SetBranchStatus("nLepton", 1); Events->SetBranchStatus("Lepton_pt", 1); Events->SetBranchStatus("Lepton_eta", 1); + Events->SetBranchStatus("Lepton_phi", 1); Events->SetBranchStatus("Lepton_pdgId", 1); Events->SetBranchStatus("mll", 1); Events->SetBranchStatus("ptll", 1); - Events->SetBranchStatus("gen_ptll", 1); double XSWeight; - Int_t nCleanJet; - // ROOT::RVec CleanJet_pt; // or using RVecF CleanJet_pt; - // std::vector* CleanJet_pt = nullptr; - float CleanJet_pt[200]; + // Gen variables float GenJet_pt[200]; - + float GenJet_eta[200]; + float GenJet_phi[200]; Int_t nGenJet; + Int_t nLeptonGen; + float LeptonGen_pt[100]; + float LeptonGen_eta[100]; + float LeptonGen_phi[100]; + float gen_ptll; + // Reco variables + Int_t nCleanJet; + float CleanJet_pt[200]; + float CleanJet_phi[200]; + float CleanJet_eta[200]; Int_t nLepton; float Lepton_pt[100]; float Lepton_eta[100]; + float Lepton_phi[100]; int Lepton_pdgId[100]; Double_t mll, ptll; - float gen_ptll; Events->SetBranchAddress("XSWeight", &XSWeight); - Events->SetBranchAddress("nCleanJet", &nCleanJet); - Events->SetBranchAddress("CleanJet_pt", CleanJet_pt); Events->SetBranchAddress("nGenJet", &nGenJet); Events->SetBranchAddress("GenJet_pt", &GenJet_pt); + Events->SetBranchAddress("GenJet_eta", &GenJet_eta); + Events->SetBranchAddress("GenJet_phi", &GenJet_phi); + Events->SetBranchAddress("nLeptonGen", &nLeptonGen); + Events->SetBranchAddress("LeptonGen_pt", LeptonGen_pt); + Events->SetBranchAddress("LeptonGen_eta", LeptonGen_eta); + Events->SetBranchAddress("LeptonGen_phi", LeptonGen_phi); + Events->SetBranchAddress("gen_ptll", &gen_ptll); + + Events->SetBranchAddress("nCleanJet", &nCleanJet); + Events->SetBranchAddress("CleanJet_pt", CleanJet_pt); + Events->SetBranchAddress("CleanJet_phi", CleanJet_phi); + Events->SetBranchAddress("CleanJet_eta", CleanJet_eta); Events->SetBranchAddress("nLepton", &nLepton); Events->SetBranchAddress("Lepton_pt", Lepton_pt); Events->SetBranchAddress("Lepton_eta", Lepton_eta); + Events->SetBranchAddress("Lepton_phi", Lepton_phi); Events->SetBranchAddress("Lepton_pdgId", Lepton_pdgId); Events->SetBranchAddress("mll", &mll); Events->SetBranchAddress("ptll", &ptll); - Events->SetBranchAddress("gen_ptll", &gen_ptll); /* @@ -177,6 +236,13 @@ void twoDhists( TH2F *gen_vs_reco_nJet = new TH2F{"gen_vs_reco_nJet", "gen_vs_reco_nJet", 10, jetbins, 10, jetbins}; gen_vs_reco_nJet->GetYaxis()->SetTitle("Reco nJet"); gen_vs_reco_nJet->GetXaxis()->SetTitle("Gen nJet"); + + TH2F *gen_vs_reco_nJetNoLep = new TH2F{"gen_vs_reco_nJet_noLeptons", "gen_vs_reco_nJet_noLeptons", 10, jetbins, 10, jetbins}; + gen_vs_reco_nJetNoLep->GetYaxis()->SetTitle("Reco nJet"); + gen_vs_reco_nJetNoLep->GetXaxis()->SetTitle("Gen nJet"); + TH2F *gen_vs_reco_nJetNoLepBoth = new TH2F{"gen_vs_reco_nJet_noLeptonsBoth", "gen_vs_reco_nJet_noLeptonsBoth", 10, jetbins, 10, jetbins}; + gen_vs_reco_nJetNoLepBoth->GetYaxis()->SetTitle("Reco nJet"); + gen_vs_reco_nJetNoLepBoth->GetXaxis()->SetTitle("Gen nJet"); int entries = Events->GetEntries(); for (unsigned int i = 0; i < Events->GetEntries(); i ++) @@ -203,9 +269,14 @@ void twoDhists( {genReco_pT_3pj->Fill(gen_ptll, ptll, XSWeight);} int nRecoJet = CountJetsAbovePt(CleanJet_pt, nCleanJet, 30.); - int nCleanGenJet = CountJetsAbovePt(GenJet_pt, nGenJet, 5.); + int nCleanGenJet = CountJetsAbovePt(GenJet_pt, nGenJet, 30.); gen_vs_reco_nJet->Fill(nCleanGenJet, nRecoJet, XSWeight); // Sum(CleanJet_pt>30) = nRecoJet // gen_vs_reco_nJet->Fill(nGenJet, Sum(CleanJet_pt>30), XSWeight); + + int nCleanGenJetNoLep = CountJetsAbovePtNoLeps(GenJet_pt, nGenJet, 30., nLeptonGen, LeptonGen_phi, LeptonGen_eta, GenJet_phi, GenJet_eta); + gen_vs_reco_nJetNoLep->Fill(nCleanGenJetNoLep, nRecoJet, XSWeight); // Sum(CleanJet_pt>30) = nRecoJet + int nRecoJetNoLep = CountJetsAbovePtNoLeps(CleanJet_pt, nCleanJet, 30., nLepton, Lepton_phi, Lepton_eta, CleanJet_phi, CleanJet_eta); + gen_vs_reco_nJetNoLepBoth->Fill(nCleanGenJetNoLep, nRecoJetNoLep, XSWeight); // Sum(CleanJet_pt>30) = nRecoJet for (int gj = 0; gj < nGenJet; gj++) {gen_jet_pt->Fill(GenJet_pt[gj]);} @@ -218,6 +289,8 @@ void twoDhists( genReco_pT_2j->Write(); genReco_pT_3pj->Write(); gen_vs_reco_nJet->Write(); + gen_vs_reco_nJetNoLep->Write(); + gen_vs_reco_nJetNoLepBoth->Write(); auto normalize_columns = [](TH2F *h) { int nx = h->GetNbinsX(); @@ -237,22 +310,22 @@ void twoDhists( }; auto draw_and_save = [&](TH2F *h, const char *qty, const char *tag) { - normalize_columns(h); - // gStyle->SetOptStat(0); + gStyle->SetOptStat(0); TCanvas *c = new TCanvas(Form("c_%s",tag), "",800,600); + normalize_columns(h); c->cd(); h->Draw("COLZ"); gPad->Update(); // Ensure stat box is created - TPaveStats *st = (TPaveStats*)h->GetListOfFunctions()->FindObject("stats"); - if (st) { - std::cout<<" Stat box found! "<SetX1NDC(0.15); // left - st->SetX2NDC(0.45); // right - st->SetY1NDC(0.7); // top - st->SetY2NDC(0.9); // bottom - st->Draw(); - } - gPad->Update(); // Ensure stat box is created + // TPaveStats *st = (TPaveStats*)h->GetListOfFunctions()->FindObject("stats"); + // if (st) { + // std::cout<<" Stat box found! "<SetX1NDC(0.15); // left + // st->SetX2NDC(0.45); // right + // st->SetY1NDC(0.7); // top + // st->SetY2NDC(0.9); // bottom + // st->Draw(); + // } + // gPad->Update(); // Ensure stat box is created TString cname = Form("genReco_%s2Dhist_%s_%d_%s_%s_%s.png", qty, tag, year, process.c_str(), algo.c_str(), WP.c_str()); c->SaveAs(cname); @@ -275,12 +348,15 @@ void twoDhists( c2->SaveAs(cname2); delete c2; - draw_and_save(genReco_pT_inclJets, "pT", "inclJets"); - draw_and_save(genReco_pT_0j, "pT", "0j"); - draw_and_save(genReco_pT_1j, "pT", "1j"); - draw_and_save(genReco_pT_2j, "pT", "2j"); - draw_and_save(genReco_pT_3pj, "pT", "3pj"); + // draw_and_save(genReco_pT_inclJets, "pT", "inclJets"); + // draw_and_save(genReco_pT_0j, "pT", "0j"); + // draw_and_save(genReco_pT_1j, "pT", "1j"); + // draw_and_save(genReco_pT_2j, "pT", "2j"); + // draw_and_save(genReco_pT_3pj, "pT", "3pj"); draw_and_save(gen_vs_reco_nJet, "nJet", ""); + draw_and_save(gen_vs_reco_nJetNoLep, "nJetNoLeptons", ""); + draw_and_save(gen_vs_reco_nJetNoLepBoth, "nJetNoLeptonsBoth", ""); + } From 10be2434d0df7b9de0d87e0b22ef8b00d4da240c Mon Sep 17 00:00:00 2001 From: CodexForster Date: Tue, 10 Mar 2026 20:49:31 +0530 Subject: [PATCH 10/13] normalization methology 2 + automation script --- ZpTreweighting/aliases.py | 10 +- ZpTreweighting/automate.py | 505 ++++++++++++++++++++++++++++++++ ZpTreweighting/extract_Zptrw.py | 122 ++++++-- 3 files changed, 610 insertions(+), 27 deletions(-) create mode 100644 ZpTreweighting/automate.py diff --git a/ZpTreweighting/aliases.py b/ZpTreweighting/aliases.py index 3707f2bc..ea17686e 100644 --- a/ZpTreweighting/aliases.py +++ b/ZpTreweighting/aliases.py @@ -1,6 +1,7 @@ import os import copy import inspect +import json configurations = os.path.realpath(inspect.getfile(inspect.currentframe())) # this file @@ -134,7 +135,14 @@ 'expr' : 'Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', } -exec(open('dyZpTrw.py', "r").read()) +# exec(open('dyZpTrw.py', "r").read()) +# aliases['DY_LO_ZpTrw'] = { +# 'expr': '('+DYrew['2022_NLO']['w'].replace('x', 'gen_Zpt')+')*(zeroJet)*(ptll < 50) + 1*(zeroJet)*(ptll >= 50)', +# 'samples': ['DY'] +# } +_dyzptrw_json = os.path.join(os.path.dirname(configurations), 'dyZpTrw.json') +with open(_dyzptrw_json) as _fj: + DYrew = json.load(_fj) aliases['DY_LO_ZpTrw'] = { 'expr': '('+DYrew['2022']['LO'].replace('x', 'gen_Zpt')+')*(zeroJet)*(ptll < 50) + 1*(zeroJet)*(ptll >= 50)', 'samples': ['DY'] diff --git a/ZpTreweighting/automate.py b/ZpTreweighting/automate.py new file mode 100644 index 00000000..d028574d --- /dev/null +++ b/ZpTreweighting/automate.py @@ -0,0 +1,505 @@ +#!/usr/bin/env python3 +""" +run_ZpTrw_workflow.py +===================== +Automated Z pT reweighting extraction workflow. + +The script orchestrates the following steps: + + Round 1 — ZpTreweighting analysis + ---------------------------------- + 1a. mkShapesRDF -c 1 compile configuration + 1b. mkShapesRDF -o 0 -f . -b 1 submit HTCondor jobs + 1c. (wait) poll condor until all jobs finish + 1d. mkShapesRDF -o 2 -f . merge individual ROOT files (hadd) + 2. extract_Zptrw.py extract the Z pT reweighting function + and overwrite dyZpTrw.json + + Round 2 — main analysis (optional) + ----------------------------------- + 3a. mkShapesRDF -c 1 compile second analysis + 3b. mkShapesRDF -o 0 -f . -b 1 submit second-round condor jobs + +Prerequisites +------------- + * mkShapesRDF must be on PATH (source the mkShapesRDF start.sh first). + * The merged ROOT output lives on EOS; EOS must be mounted (lxplus default). + * Python packages required by extract_Zptrw.py must be available (ROOT, + mplhep, matplotlib, numpy). + +Typical usage +------------- + # Run from inside ZpTreweighting/ or give the folder explicitly: + python run_ZpTrw_workflow.py + + # Also kick off the second-round analysis immediately after: + python run_ZpTrw_workflow.py --second-analysis ../HWW/ggH_SF/2022 + + # Skip submission if jobs were already submitted: + python run_ZpTrw_workflow.py --skip-submit + + # Dry run to see what commands would be executed: + python run_ZpTrw_workflow.py --dry-run +""" + +import argparse +import glob +import os +import re +import subprocess +import sys +import time + + +# --------------------------------------------------------------------------- +# Pretty printing helpers +# --------------------------------------------------------------------------- + +def banner(msg): + width = max(60, len(msg) + 4) + print("\n" + "=" * width) + print(f" {msg}") + print("=" * width) + + +def info(msg): + print(f" {msg}") + + +# --------------------------------------------------------------------------- +# Command execution +# --------------------------------------------------------------------------- + +def run_cmd(cmd, dry_run=False, cwd=None): + """Print and (optionally) execute *cmd*. + + *cmd* may be a list of strings or a single shell string. + Returns the process exit code (always 0 in dry-run mode). + """ + display = " ".join(cmd) if isinstance(cmd, list) else cmd + info(f"$ {display}") + if dry_run: + return 0 + result = subprocess.run(cmd, cwd=cwd, shell=isinstance(cmd, str)) + return result.returncode + + +def run_cmd_output(cmd, cwd=None): + """Run *cmd* and return *(returncode, stdout, stderr)* as strings.""" + result = subprocess.run( + cmd, + cwd=cwd, + capture_output=True, + text=True, + shell=isinstance(cmd, str), + ) + return result.returncode, result.stdout, result.stderr + + +# --------------------------------------------------------------------------- +# HTCondor job tracking +# --------------------------------------------------------------------------- + +def _get_cluster_ids_from_logs(batch_dir, tag): + """Return the set of condor cluster IDs found in the job log files. + + Log files are expected at ``{batch_dir}/{tag}/**/log.txt`` (the standard + layout produced by mkShapesRDF). The cluster ID is extracted from lines + matching the condor event-log format:: + + 000 (CLUSTERID.PROCID.SUBPROCID) DATE TIME Job submitted from host... + """ + log_pattern = os.path.join(batch_dir, tag, "**", "log.txt") + log_files = glob.glob(log_pattern, recursive=True) + cluster_ids = set() + for log_file in log_files: + try: + with open(log_file) as fh: + for line in fh: + m = re.match(r"000 \((\d+)\.\d+\.\d+\)", line) + if m: + cluster_ids.add(m.group(1)) + except OSError: + pass + return cluster_ids + + +def _count_condor_jobs_in_cluster(cluster_id): + """Return the number of jobs still in the condor queue for *cluster_id*. + + Runs ``condor_q `` and parses the summary line, e.g.:: + + 3 jobs; 0 completed; 0 removed; 3 idle; 0 running; 0 held; 0 suspended + """ + rc, stdout, _ = run_cmd_output(["condor_q", str(cluster_id)]) + if rc != 0: + # cluster is gone (all jobs completed or removed) + return 0 + m = re.search(r"(\d+) jobs?", stdout) + return int(m.group(1)) if m else 0 + + +def wait_for_condor_jobs(batch_dir, tag, poll_interval=120, dry_run=False): + """Block until all HTCondor jobs for *tag* are no longer queued. + + Parameters + ---------- + batch_dir: + Directory that contains the ``{tag}/`` sub-tree of condor job folders. + tag: + Analysis tag (matches the sub-directory name inside ``batch_dir``). + poll_interval: + Seconds to wait between condor_q polls. + dry_run: + If True, return immediately without checking anything. + """ + # Seconds to wait after submission before the first condor_q poll, so + # HTCondor has time to register the newly submitted jobs. + CONDOR_REGISTRATION_DELAY = 10 + + if dry_run: + info("[dry-run] Skipping condor wait.") + return + + # Give HTCondor a few seconds to register newly submitted jobs. + time.sleep(CONDOR_REGISTRATION_DELAY) + + cluster_ids = _get_cluster_ids_from_logs(batch_dir, tag) + if not cluster_ids: + info("WARNING: No condor cluster IDs found in log files. " + "Waiting 60 s and retrying once...") + time.sleep(60) + cluster_ids = _get_cluster_ids_from_logs(batch_dir, tag) + + if not cluster_ids: + info("WARNING: Still no cluster IDs found. " + "Assuming jobs have already completed or were submitted " + "outside HTCondor.") + return + + info(f"Tracking condor cluster(s): {', '.join(sorted(cluster_ids))}") + + while True: + remaining = { + cid for cid in cluster_ids + if _count_condor_jobs_in_cluster(cid) > 0 + } + if not remaining: + info("All condor jobs have completed.") + return + + total = sum(_count_condor_jobs_in_cluster(c) for c in remaining) + info( + f"[{time.strftime('%H:%M:%S')}] " + f"{total} job(s) still queued in " + f"cluster(s) {', '.join(sorted(remaining))}. " + f"Polling again in {poll_interval} s..." + ) + time.sleep(poll_interval) + + +# --------------------------------------------------------------------------- +# Configuration reader +# --------------------------------------------------------------------------- + +def read_configuration(cfg_file): + """Execute *cfg_file* and return a dict with analysis settings. + + Reads ``tag``, ``outputFolder``, ``outputFile``, and ``batchFolder`` + from ``configuration.py``. + """ + ns = { + "__file__": os.path.abspath(cfg_file), + "os": os, + "sys": sys, + } + try: + with open(cfg_file) as fh: + exec(compile(fh.read(), cfg_file, "exec"), ns) + except Exception as exc: + # configuration.py may call os.getlogin() which can fail in some + # environments; fall through with whatever was captured so far. + info(f"WARNING: Error while parsing {cfg_file}: {exc}") + + tag = ns.get("tag", "ZpTreweighting") + output_folder = ns.get( + "outputFolder", + os.path.join( + "/eos/user", + os.environ.get("USER", "unknown")[0], + os.environ.get("USER", "unknown"), + "mkShapesRDF_rootfiles", + tag, + "rootFile", + ), + ) + return { + "tag": tag, + "outputFolder": output_folder.rstrip("/"), + "outputFile": ns.get("outputFile", f"mkShapes__{tag}.root"), + "batchFolder": ns.get("batchFolder", "condor"), + } + + +# --------------------------------------------------------------------------- +# Workflow phases +# --------------------------------------------------------------------------- + +def phase1_submit(zptrw_dir, dry_run=False): + """Phase 1a+1b — compile and submit condor jobs.""" + banner("Phase 1a: Compile ZpTreweighting configuration") + rc = run_cmd(["mkShapesRDF", "-c", "1"], dry_run=dry_run, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: mkShapesRDF -c 1 failed (exit code {rc})") + + banner("Phase 1b: Submit ZpTreweighting condor jobs") + rc = run_cmd( + ["mkShapesRDF", "-o", "0", "-f", ".", "-b", "1"], + dry_run=dry_run, + cwd=zptrw_dir, + ) + if rc != 0: + sys.exit(f"ERROR: mkShapesRDF -o 0 failed (exit code {rc})") + + +def phase1_wait(zptrw_dir, cfg, poll_interval=120, dry_run=False): + """Phase 1c — wait for all condor jobs to finish.""" + banner("Phase 1c: Waiting for HTCondor jobs to complete") + batch_dir = os.path.join(zptrw_dir, cfg["batchFolder"]) + wait_for_condor_jobs( + batch_dir=batch_dir, + tag=cfg["tag"], + poll_interval=poll_interval, + dry_run=dry_run, + ) + + +def phase1_merge(zptrw_dir, dry_run=False): + """Phase 1d — merge individual job ROOT files (hadd).""" + banner("Phase 1d: Merge ROOT files") + rc = run_cmd( + ["mkShapesRDF", "-o", "2", "-f", "."], + dry_run=dry_run, + cwd=zptrw_dir, + ) + if rc != 0: + sys.exit(f"ERROR: Phase 1d - mkShapesRDF -o 2 (merge) failed (exit code {rc})") + + +def phase2_extract(zptrw_dir, cfg, year="2022", sample_type="LO", dry_run=False): + """Phase 2 — run extract_Zptrw.py to derive weights and update dyZpTrw.json.""" + banner("Phase 2: Extract Z pT reweighting function → update dyZpTrw.json") + + merged_file = os.path.join(cfg["outputFolder"], cfg["outputFile"]) + dyzptrw_json = os.path.join(zptrw_dir, "dyZpTrw.json") + extract_script = os.path.join(zptrw_dir, "extract_Zptrw.py") + + if not dry_run and not os.path.exists(merged_file): + sys.exit( + f"ERROR: Merged ROOT file not found:\n" + f" {merged_file}\n" + f"Run 'mkShapesRDF -o 2 -f .' in {zptrw_dir} first." + ) + + cmd = [ + sys.executable, extract_script, + "-f", + "--input", merged_file, + "--write-json", dyzptrw_json, + "--year", year, + "--sample-type", sample_type, + ] + rc = run_cmd(cmd, dry_run=dry_run, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: extract_Zptrw.py failed (exit code {rc})") + + info(f"dyZpTrw.json updated → {dyzptrw_json}") + + +def phase3_second_round(second_dir, dry_run=False): + """Phase 3 — compile and submit the second-round analysis.""" + banner(f"Phase 3: Second-round mkShapesRDF in\n {second_dir}") + + rc = run_cmd(["mkShapesRDF", "-c", "1"], dry_run=dry_run, cwd=second_dir) + if rc != 0: + sys.exit( + f"ERROR: mkShapesRDF -c 1 failed in {second_dir} (exit code {rc})" + ) + + rc = run_cmd( + ["mkShapesRDF", "-o", "0", "-f", ".", "-b", "1"], + dry_run=dry_run, + cwd=second_dir, + ) + if rc != 0: + sys.exit( + f"ERROR: mkShapesRDF -o 0 failed in {second_dir} (exit code {rc})" + ) + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + +def parse_args(): + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + # ---- paths ---- + parser.add_argument( + "--zptrw-folder", + default=".", + metavar="DIR", + help="Path to the ZpTreweighting analysis folder " + "(default: current directory).", + ) + parser.add_argument( + "--second-analysis", + default=".", + metavar="DIR", + help="Path to the second-round analysis folder. " + "When given, Phase 3 compiles and submits jobs there after " + "dyZpTrw.py has been updated.", + ) + + # ---- physics ---- + parser.add_argument( + "--year", + default="2022", + help="Year key written to DYrew in dyZpTrw.json (default: '2022').", + ) + parser.add_argument( + "--sample-type", + default="LO", + help="Sample-type key written to DYrew in dyZpTrw.json " + "(default: 'LO').", + ) + + # ---- condor polling ---- + parser.add_argument( + "--poll-interval", + type=int, + default=120, + metavar="SECONDS", + help="Seconds between condor_q polls while waiting for jobs " + "(default: 120).", + ) + + # ---- skip flags ---- + skip = parser.add_argument_group("skip flags (for re-running partial workflow)") + skip.add_argument( + "--skip-submit", + action="store_true", + help="Skip Phase 1a+1b (assume jobs are already running or done).", + ) + skip.add_argument( + "--skip-wait", + action="store_true", + help="Skip Phase 1c (assume all jobs have already finished).", + ) + skip.add_argument( + "--skip-merge", + action="store_true", + help="Skip Phase 1d (assume the merged ROOT file already exists).", + ) + skip.add_argument( + "--skip-extract", + action="store_true", + help="Skip Phase 2 (assume dyZpTrw.json is already up to date).", + ) + + # ---- misc ---- + parser.add_argument( + "--dry-run", + action="store_true", + help="Print every command that would be run without executing it.", + ) + + return parser.parse_args() + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main(): + args = parse_args() + + zptrw_dir = os.path.abspath(args.zptrw_folder) + if not os.path.isdir(zptrw_dir): + sys.exit(f"ERROR: ZpTreweighting folder not found: {zptrw_dir}") + + cfg_file = os.path.join(zptrw_dir, "configuration.py") + if not os.path.exists(cfg_file): + sys.exit(f"ERROR: configuration.py not found in {zptrw_dir}") + + banner("Z pT reweighting workflow") + info(f"ZpTreweighting folder : {zptrw_dir}") + if args.dry_run: + info("*** DRY-RUN mode — no commands will be executed ***") + + cfg = read_configuration(cfg_file) + info(f"tag : {cfg['tag']}") + info(f"outputFolder : {cfg['outputFolder']}") + info(f"outputFile : {cfg['outputFile']}") + info(f"batchFolder : {cfg['batchFolder']}") + + # ---- Phase 1a+1b: Submit ---- + if not args.skip_submit: + phase1_submit(zptrw_dir, dry_run=args.dry_run) + else: + info("\n[skip-submit] Skipping job submission.") + + # ---- Phase 1c: Wait ---- + if not args.skip_wait and not args.skip_submit: + phase1_wait( + zptrw_dir, + cfg, + poll_interval=args.poll_interval, + dry_run=args.dry_run, + ) + elif args.skip_wait: + info("\n[skip-wait] Skipping condor wait.") + + # ---- Phase 1d: Merge ---- + if not args.skip_merge: + phase1_merge(zptrw_dir, dry_run=args.dry_run) + else: + info("\n[skip-merge] Skipping ROOT file merge.") + + # ---- Phase 2: Extract + Update ---- + if not args.skip_extract: + phase2_extract( + zptrw_dir, + cfg, + year=args.year, + sample_type=args.sample_type, + dry_run=args.dry_run, + ) + else: + info("\n[skip-extract] Skipping weight extraction and dyZpTrw.json update.") + + # ---- Phase 3 (optional): Second analysis ---- + if args.second_analysis: + second_dir = os.path.abspath(args.second_analysis) + if not os.path.isdir(second_dir): + sys.exit(f"ERROR: Second-analysis folder not found: {second_dir}") + phase3_second_round(second_dir, dry_run=args.dry_run) + + banner("Workflow complete!") + if args.second_analysis: + info("Second-round jobs submitted. Next steps:") + info(f" Monitor : mkShapesRDF -o 1 -f {args.second_analysis}") + info(f" Merge : mkShapesRDF -o 2 -f {args.second_analysis}") + else: + info("dyZpTrw.json has been updated.") + info("To run the second-round analysis:") + info(" cd ") + info(" mkShapesRDF -c 1") + info(" mkShapesRDF -o 0 -f . -b 1") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ZpTreweighting/extract_Zptrw.py b/ZpTreweighting/extract_Zptrw.py index 2d1d9340..c81805b5 100644 --- a/ZpTreweighting/extract_Zptrw.py +++ b/ZpTreweighting/extract_Zptrw.py @@ -1,6 +1,8 @@ # ================================= # Danush Shekar (UIC), 9Dec25 # ================================= +import json +import os import ROOT import mplhep as hep import matplotlib.pyplot as plt @@ -13,13 +15,35 @@ style["font.size"] = 18 plt.style.use(style) +def calc_norm_factor(dy_hist, fitting_function, fit_params): + numerator = 0.0 + denominator = 0.0 + first_bin = dy_hist.FindBin(0) + last_bin = dy_hist.FindBin(50) + print("\nNOTE: Using fit function (", fitting_function,") to calculate normalization factor.\n") # [0]*TMath::Erf((x-[1])/[2]) + [3]*x + [4]*x**2 + [5] + for bin_idx in range(first_bin, last_bin + 1): + mc_events = dy_hist.GetBinContent(bin_idx) + # weight = histo_ratio.GetBinContent(bin_idx) + binCenter = dy_hist.GetXaxis().GetBinCenter(bin_idx) + weight = fit_params[0]*ROOT.TMath.Erf((binCenter - fit_params[1])/fit_params[2]) + fit_params[3]*binCenter + fit_params[4]*binCenter**2 + fit_params[5] + numerator += mc_events + denominator += mc_events * weight + norm_factor = numerator / denominator if denominator != 0 else 1.0 + print("Normalization factor:", norm_factor) + return norm_factor + parser = argparse.ArgumentParser(description='Extract data and fit with Gaussian.') parser.add_argument('-f', action='store_true', help='Fit the ratio plot using Erf.') +parser.add_argument('-n', type=int, default=0, help='Normalization method:\n1: Ratio of integral of MC over weights*MC.\n2: Normalize MC to data integral before calculating rw factor.') +parser.add_argument('-i', '--input', default='mkShapes__ZpTreweighting.root', help='Path to the merged ROOT file (default: mkShapes__ZpTreweighting.root)') +parser.add_argument('--write-json', default=None, help='If given, write the updated dyZpTrw.json to this path after a successful fit (requires -f). The file is overwritten.') +parser.add_argument('--year', default='2022', help="Year key in the DYrew dict written to dyZpTrw.json (default: '2022')") +parser.add_argument('--sample-type', default='LO', help="Sample-type key in the DYrew dict written to dyZpTrw.json (default: 'LO')") args = parser.parse_args() -root_file = ROOT.TFile("mkShapes__ZpTreweighting.root") -# root_file = ROOT.TFile("mkShapes__beforeZpTreweighting_highLepPtThreshold_30_18.root") -zee_dir = root_file.Get("Zmm_0j") +root_file = ROOT.TFile(args.input) +channel = "Zmm_0j" +zee_dir = root_file.Get(channel) ptll_dir = zee_dir.Get("ptll") histo_DY = ptll_dir.Get("histo_DY") @@ -38,32 +62,25 @@ # histo_trueData.Rebin(4) # histo_DY.Rebin(4) +# Calculate the integral/sum of histo_DY and histo_ratio for x axis in [0, 50) +integral_histo_DY = histo_DY.Integral(histo_DY.FindBin(0), histo_DY.FindBin(50) - 1) +integral_histo_DATA = histo_trueData.Integral(histo_trueData.FindBin(0), histo_trueData.FindBin(50) - 1) +norm_factor2 = integral_histo_DATA/integral_histo_DY +print("Normalization factor 2:", norm_factor2) +if args.n == 2: + histo_DY.Scale(norm_factor2) + integral_histo_DYscaled = histo_DY.Integral(histo_DY.FindBin(0), histo_DY.FindBin(50) - 1) + # Create a ratio plot of DATA to DY histo_ratio = histo_trueData.Clone("histo_ratio") histo_ratio.Divide(histo_DY) -# Calculate the integral/sum of histo_DY and histo_ratio for x axis in [0, 50) -integral_histo_DY = histo_DY.Integral(histo_DY.FindBin(0), histo_DY.FindBin(50) - 1) integral_histo_ratio = histo_ratio.Integral(histo_ratio.FindBin(0), histo_ratio.FindBin(50) - 1) -numerator = 0.0 -denominator = 0.0 -first_bin = histo_DY.FindBin(0) -last_bin = histo_DY.FindBin(50) - -for bin_idx in range(first_bin, last_bin + 1): - mc_events = histo_DY.GetBinContent(bin_idx) - weight = histo_ratio.GetBinContent(bin_idx) - numerator += mc_events - denominator += mc_events * weight - -norm_factor = numerator / denominator if denominator != 0 else 1.0 -print("Normalization factor:", norm_factor) - # fitting_functions = ["[0]*x**6 + [1]*x**5 + [2]*x**4 + [3]*x**3 + [4]*x**2 + [5]*x + [6]", "[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x**2)"] -fitting_functions = ["[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x**2 + [6])"] -initial_guesses = [[1.0, 0.0, 5.0, 10.0, 0.0, 0.0, 1.0]] -save_name_suffixes = ["erf_poly2"] +fitting_functions = ["([0]*TMath::Erf((x-[1])/[2]) + [3]*x + [4]*x**2 + [5])"] +initial_guesses = [[0.0, 5.0, 10.0, 0.0, 0.0, 1.0]] +save_name_suffixes = [channel] for fitfunc, initguess, savename in zip(fitting_functions, initial_guesses, save_name_suffixes): c = ROOT.TCanvas("c", "c", 1000, 1000) c.Divide(1,2) @@ -99,6 +116,11 @@ label.SetTextSize(0.040) label.DrawLatex(0.12, 0.92, "#bf{CMS} #it{Preliminary}") label.DrawLatex(0.55, 0.92, "L = 8.2 fb^{-1} (#sqrt{s} = 13.6 TeV)") + label.DrawLatex(0.15, 0.2, f"num(DY) events in (0,50) GeV = {integral_histo_DY:.3f}") + label.DrawLatex(0.15, 0.15, f"num(DATA) events in (0,50) GeV = {integral_histo_DATA:.3f}") + if args.n == 2: + label.DrawLatex(0.15, 0.1, f"num(DY normalized) events in (0,50) GeV = {integral_histo_DYscaled:.3f}") + leg = ROOT.TLegend(0.60, 0.70, 0.88, 0.88) leg.SetBorderSize(0) @@ -175,7 +197,7 @@ fit_func.Draw("SAME") fit_func.Print("V") c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.pdf") - c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.png") + # c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.png") # Commented out as plot quality is very bad c_ratio_only = ROOT.TCanvas("c_ratio_only", "c_ratio_only", 800, 800) @@ -202,7 +224,10 @@ # Display fit function and parameters latex.DrawLatex(0.15, 0.35, f"f(x) = {func_formula}") latex.DrawLatex(0.15, 0.3, param_str) - latex.DrawLatex(0.15, 0.25, f"Normalization factor = {norm_factor:.2f}") + if args.n == 1: + norm_factor = calc_norm_factor(histo_DY, fitfunc, param_values) + latex.DrawLatex(0.15, 0.25, f"Normalization factor = {norm_factor:.2f}") + print(f"Normalization factor = {norm_factor}") formula = fit_func.GetTitle() # e.g., "[0]*x + [1]" n_params = fit_func.GetNpar() params = [fit_func.GetParameter(i) for i in range(n_params)] @@ -212,7 +237,52 @@ print(f"Fit function with parameters: {formula}") c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.pdf") - c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.png") + # c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.png") print(f"Integral of DY histogram from 0 to 50 GeV: {integral_histo_DY}") print(f"Integral of ratio histogram from 0 to 50 GeV: {integral_histo_ratio}") -print(f"Normalization factor = {norm_factor}") \ No newline at end of file + +# Update dyZpTrw.json +if args.write_json and args.f: + wrote = False + # 'fit_func', 'fit_result', 'fitfunc' are in scope from the last for-loop + # iteration (Python loop variables persist after the loop). + try: + if fit_result and fit_result.IsValid(): + # Build a ROOT / C++ compatible formula string with full precision. + root_formula = fitfunc # e.g. "[0]*TMath::Erf(...) + [3]*x + [4]*x**2 + [5]" + n_params = fit_func.GetNpar() + params = [fit_func.GetParameter(i) for i in range(n_params)] + for i, p in enumerate(params): + root_formula = root_formula.replace(f"[{i}]", f"{p:.6f}") + # Convert Python-style x**2 to ROOT / C++ TMath::Sq(x) + root_formula = root_formula.replace("x**2", "TMath::Sq(x)") + # Tidy up double signs that can appear after parameter substitution + root_formula = root_formula.replace("+ -", "- ") + root_formula = root_formula.replace("- -", "+ ") + # Prepend the integral normalization factor if methodology 2 is chosen + if args.n == 1: + full_expr = f"{norm_factor}*{root_formula}" + + # Read the existing JSON so other years/types are preserved. + existing = {} + if os.path.exists(args.write_json): + try: + with open(args.write_json) as _fj: + existing = json.load(_fj) + except json.JSONDecodeError as _e: + print(f"WARNING: Existing JSON file '{args.write_json}' is malformed " + f"({_e}); it will be overwritten.") + # Update only the requested year / sample-type key. + existing.setdefault(args.year, {})[args.sample_type] = full_expr + + with open(args.write_json, "w") as _fj: + json.dump(existing, _fj, indent=4) + _fj.write("\n") + print(f"\nWrote updated dyZpTrw.json → {args.write_json}") + print(f" [{args.year}][{args.sample_type}]: {full_expr}") + wrote = True + else: + print("\nWARNING: Fit did not converge; dyZpTrw.json was NOT updated.") + except NameError: + print("\nWARNING: No fit results in scope (was -f passed?). " + "dyZpTrw.json was NOT updated.") \ No newline at end of file From 522596924687767b78bf60c2c1d40e52c3325764 Mon Sep 17 00:00:00 2001 From: CodexForster Date: Tue, 14 Apr 2026 13:00:34 -0500 Subject: [PATCH 11/13] nJet bin categories, automated reweighting script --- ZpTreweighting/{ => 2022_v12}/aliases.py | 6 +- ZpTreweighting/2022_v12/aliases_withBtagSF.py | 245 ++++++ ZpTreweighting/2022_v12/aliases_woBtagSF.py | 263 +++++++ ZpTreweighting/2022_v12/automate.py | 744 ++++++++++++++++++ .../{ => 2022_v12}/configuration.py | 0 ZpTreweighting/{ => 2022_v12}/cuts.py | 3 +- ZpTreweighting/2022_v12/dyZpTrw.json | 17 + .../{ => 2022_v12}/extract_Zptrw.py | 171 ++-- ZpTreweighting/{ => 2022_v12}/nuisances.py | 0 ZpTreweighting/{ => 2022_v12}/plot.py | 0 ZpTreweighting/{ => 2022_v12}/samples.py | 10 +- ZpTreweighting/{ => 2022_v12}/structure.py | 0 ZpTreweighting/{ => 2022_v12}/variables.py | 2 +- ZpTreweighting/2023_v12/aliases.py | 261 ++++++ ZpTreweighting/2023_v12/automate.py | 744 ++++++++++++++++++ ZpTreweighting/2023_v12/configuration.py | 90 +++ ZpTreweighting/2023_v12/cuts.py | 59 ++ ZpTreweighting/2023_v12/dyZpTrw.json | 17 + ZpTreweighting/2023_v12/dyZpTrw.py | 6 + ZpTreweighting/2023_v12/extract_Zptrw.py | 311 ++++++++ ZpTreweighting/2023_v12/nuisances.py | 52 ++ ZpTreweighting/2023_v12/plot.py | 67 ++ ZpTreweighting/2023_v12/samples.py | 205 +++++ ZpTreweighting/2023_v12/structure.py | 30 + ZpTreweighting/2023_v12/variables.py | 176 +++++ ZpTreweighting/automate.py | 505 ------------ ZpTreweighting/dyZpTrw.py | 5 - ZpTreweighting/readme.md | 6 + 28 files changed, 3404 insertions(+), 591 deletions(-) rename ZpTreweighting/{ => 2022_v12}/aliases.py (96%) create mode 100644 ZpTreweighting/2022_v12/aliases_withBtagSF.py create mode 100644 ZpTreweighting/2022_v12/aliases_woBtagSF.py create mode 100644 ZpTreweighting/2022_v12/automate.py rename ZpTreweighting/{ => 2022_v12}/configuration.py (100%) rename ZpTreweighting/{ => 2022_v12}/cuts.py (94%) create mode 100644 ZpTreweighting/2022_v12/dyZpTrw.json rename ZpTreweighting/{ => 2022_v12}/extract_Zptrw.py (67%) rename ZpTreweighting/{ => 2022_v12}/nuisances.py (100%) rename ZpTreweighting/{ => 2022_v12}/plot.py (100%) rename ZpTreweighting/{ => 2022_v12}/samples.py (93%) rename ZpTreweighting/{ => 2022_v12}/structure.py (100%) rename ZpTreweighting/{ => 2022_v12}/variables.py (99%) create mode 100644 ZpTreweighting/2023_v12/aliases.py create mode 100644 ZpTreweighting/2023_v12/automate.py create mode 100644 ZpTreweighting/2023_v12/configuration.py create mode 100644 ZpTreweighting/2023_v12/cuts.py create mode 100644 ZpTreweighting/2023_v12/dyZpTrw.json create mode 100644 ZpTreweighting/2023_v12/dyZpTrw.py create mode 100644 ZpTreweighting/2023_v12/extract_Zptrw.py create mode 100644 ZpTreweighting/2023_v12/nuisances.py create mode 100644 ZpTreweighting/2023_v12/plot.py create mode 100644 ZpTreweighting/2023_v12/samples.py create mode 100644 ZpTreweighting/2023_v12/structure.py create mode 100644 ZpTreweighting/2023_v12/variables.py delete mode 100644 ZpTreweighting/automate.py delete mode 100644 ZpTreweighting/dyZpTrw.py diff --git a/ZpTreweighting/aliases.py b/ZpTreweighting/2022_v12/aliases.py similarity index 96% rename from ZpTreweighting/aliases.py rename to ZpTreweighting/2022_v12/aliases.py index ea17686e..ac657ceb 100644 --- a/ZpTreweighting/aliases.py +++ b/ZpTreweighting/2022_v12/aliases.py @@ -143,8 +143,10 @@ _dyzptrw_json = os.path.join(os.path.dirname(configurations), 'dyZpTrw.json') with open(_dyzptrw_json) as _fj: DYrew = json.load(_fj) -aliases['DY_LO_ZpTrw'] = { - 'expr': '('+DYrew['2022']['LO'].replace('x', 'gen_Zpt')+')*(zeroJet)*(ptll < 50) + 1*(zeroJet)*(ptll >= 50)', +aliases['DY_NLO_ZpTrw'] = { + 'expr': '(' + DYrew['2022']['NLO_0j'].replace('x', 'gen_Zpt') + ')*(zeroJet)' + + ' + (' + DYrew['2022']['NLO_1j'].replace('x', 'gen_Zpt') + ')*(oneJet&& Alt(CleanJet_pt,1,0)<30)' + + ' + (' + DYrew['2022']['NLO_2j'].replace('x', 'gen_Zpt') + ')*(multiJet)', 'samples': ['DY'] } diff --git a/ZpTreweighting/2022_v12/aliases_withBtagSF.py b/ZpTreweighting/2022_v12/aliases_withBtagSF.py new file mode 100644 index 00000000..6ae664a1 --- /dev/null +++ b/ZpTreweighting/2022_v12/aliases_withBtagSF.py @@ -0,0 +1,245 @@ +import os +import copy +import inspect +import ROOT + +ROOT.gSystem.Load("libGpad.so") +ROOT.gSystem.Load("libGraf.so") + +configurations = os.path.realpath(inspect.getfile(inspect.currentframe())) # this file +macros = os.path.dirname(configurations) + '/macros/' +btagmaps = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(configurations)))) + '/utils/data/btag' +print(macros) +print(btagmaps) + +aliases = {} +aliases = OrderedDict() + +mc = [skey for skey in samples if skey not in ('Fake', 'DATA', 'Dyemb', 'DATA_EG', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] +# Commented out as not used (DS, 19Nov25) +# mc_emb = [skey for skey in samples if skey not in ('Fake', 'DATA', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] + +# Using LepSF2l__ele_cutBased_LooseID_tthMVA_Run3__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 from latest git repo push (https://github.com/latinos/PlotsConfigurationsRun3/blob/f8a0f50dfe6301543203d9d260ad721204a2739f/ControlRegions/DY/2022_v12/aliases.py#L14-L15) +eleWP = 'cutBased_LooseID_tthMVA_Run3' +muWP = 'cut_TightID_pfIsoTight_HWW_tthmva_67' + +aliases['LepWPCut'] = { + 'expr': 'LepCut2l__ele_'+eleWP+'__mu_'+muWP, + 'samples': mc + ['DATA'], +} + +aliases['LepWPSF'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__mu_'+muWP, + 'samples': mc +} + +# gen-matching to prompt only (GenLepMatch2l matches to *any* gen lepton) +aliases['PromptGenLepMatch2l'] = { + 'expr': 'Alt(Lepton_promptgenmatched, 0, 0) * Alt(Lepton_promptgenmatched, 1, 0)', + 'samples': mc +} + +aliases['gen_Zpt'] = { + # 'linesToAdd': [".L /afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+"], + # 'linesToAdd': ['.L /eos/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+'], + 'linesToAdd': [ + """ +#ifndef getGenZpt +#define getGenZpt + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" + +using namespace ROOT; +using namespace ROOT::VecOps; + +double GetGenZpt( + int nGenPart, + RVecF GenPart_pt, + RVecI GenPart_pdgId, + RVecI GenPart_genPartIdxMother, + RVecI GenPart_statusFlags, + float gen_ptll + ){ + + + + // Find Gen pT of Z decaying into leptons + unsigned nGen = nGenPart; + std::vector LepCands{}; + std::vector MotherIdx{}; + std::vector MotherPdgId{}; + int pdgId, sFlag, MIdx; + bool hasZ = false; + //std::cout << "==========" << std::endl; + for (unsigned iGen{0}; iGen != nGen; ++iGen){ + pdgId = std::abs(GenPart_pdgId[iGen]); + sFlag = GenPart_statusFlags[iGen]; + //std::cout << pdgId << " ; " << sFlag << " ; " << GenPart_pt->At(iGen) << " ; " << GenPart_genPartIdxMother->At(iGen) << std::endl; + if (((pdgId == 11) || (pdgId == 13) || (pdgId == 15)) && ((sFlag >> 0 & 1) || (sFlag >> 2 & 1) || (sFlag >> 3 & 1) || (sFlag >> 4 & 1))){ + LepCands.push_back(iGen); + MIdx = GenPart_genPartIdxMother[iGen]; + MotherIdx.push_back(MIdx); + if (MIdx > -1){ + MotherPdgId.push_back(GenPart_pdgId[MIdx]); + if (GenPart_pdgId[MIdx]==23) hasZ = true; + }else{ + MotherPdgId.push_back(0); + } + } + } + + //std::cout << "Check:" << std::endl; + for (unsigned iGen{0}; iGen != LepCands.size(); ++iGen){ + for (unsigned jGen{0}; jGen != LepCands.size(); ++jGen){ + if (jGen <= iGen) continue; + //std::cout << iGen << " ; " << MotherIdx[iGen] << " ; " << jGen << " ; " << MotherIdx[jGen] << " ; " << MotherPdgId[iGen] << " ; " << hasZ << std::endl; + // Some DY samples generate the Z; others have the two leptons produced directly -> motherId is 0 for those events + if (hasZ){ + if (MotherIdx[iGen] == MotherIdx[jGen] && MotherPdgId[iGen] == 23) return GenPart_pt[MotherIdx[iGen]]; + }else{ + if (MotherIdx[iGen] == MotherIdx[jGen] && MotherIdx[iGen] == 0) return GenPart_pt[MotherIdx[iGen]]; + } + } + } + //std::cout << "Falling back!" << std::endl; + return gen_ptll; + +} + +#endif + """], + 'class': 'GetGenZpt', + 'args': 'nGenPart, GenPart_pt, GenPart_pdgId, GenPart_genPartIdxMother, GenPart_statusFlags, gen_ptll', + # 'expr': 'gen_ptll', + 'samples': mc +} + +# Jet bins +# using Alt(CleanJet_pt, n, 0) instead of Sum(CleanJet_pt >= 30) because jet pt ordering is not strictly followed in JES-varied samples + +# No jet with pt > 30 GeV +aliases['zeroJet'] = { + 'expr': 'Alt(CleanJet_pt, 0, 0) < 30.' +} + +aliases['oneJet'] = { + 'expr': 'Alt(CleanJet_pt, 0, 0) > 30.' +} + +aliases['multiJet'] = { + 'expr': 'Alt(CleanJet_pt, 1, 0) > 30.' +} + +aliases['noJetInHorn'] = { + 'expr' : 'Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', +} + +######################################################################## +# B-Tagging WP: https://btv-wiki.docs.cern.ch/ScaleFactors/Run3Summer22/ +######################################################################## + +# Algo / WP / WP cut +btagging_WPs = { + "DeepFlavB" : { + "loose" : "0.0583", + "medium" : "0.3086", + "tight" : "0.7183", + "xtight" : "0.8111", + "xxtight" : "0.9512", + }, + "RobustParTAK4B" : { + "loose" : "0.0849", + "medium" : "0.4319", + "tight" : "0.8482", + "xtight" : "0.9151", + "xxtight" : "0.9874", + }, + "PNetB" : { + "loose" : "0.0470", + "medium" : "0.2450", + "tight" : "0.6734", + "xtight" : "0.7862", + "xxtight" : "0.9610", + } +} + +# Algo / SF name +btagging_SFs = { + "DeepFlavB" : "deepjet", + "RobustParTAK4B" : "partTransformer", + "PNetB" : "partNet", +} + +# Algorithm and WP selection +bAlgo = 'PNetB' # ['DeepFlavB','RobustParTAK4B','PNetB'] +WP = 'loose' # ['loose','medium','tight','xtight','xxtight'] + +# Access information from dictionaries +bWP = btagging_WPs[bAlgo][WP] +bSF = btagging_SFs[bAlgo] + +# # B tagging selections and scale factors +aliases['bVeto'] = { + 'expr': f'Sum(CleanJet_pt > 20. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) == 0' +} + +aliases['bReq'] = { + 'expr': f'Sum(CleanJet_pt > 30. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) >= 1' +} + +########################################################################## +# End of b tagging +########################################################################## + +# Top control region +aliases['topcr'] = { + 'expr': 'mtw2>30 && mll>50 && ((zeroJet && !bVeto) || bReq)' +} + +WP_eval = 'L' # ['L', 'M', 'T', 'XT', 'XXT'] +tagger = 'particleNet' +eff_map_year = '2022' # ['2022', '2022EE', '2023', '2023BPix', '2024] +year = 'Run3-22CDSep23-Summer22-NanoAODv12' # ['Run3-22CDSep23-Summer22-NanoAODv12', 'Run3-22EFGSep23-Summer22EE-NanoAODv12, 'Run3-23CSep23-Summer23-NanoAODv12', 'Run3-23DSep23-Summer23BPix-NanoAODv12', 'Run3-24CDEReprocessingFGHIPrompt-Summer24-NanoAODv15'] + +for flavour in ['bc', 'light']: + for shift in ['central', 'up_uncorrelated', 'down_uncorrelated', 'up_correlated', 'down_correlated']: + btagsf = 'btagSF' + flavour + if shift != 'central': + btagsf += '_' + shift + aliases[btagsf] = { + 'linesToAdd': [f'#include "{macros}evaluate_btagSF{flavour}.cc"'], + 'linesToProcess': [f"ROOT.gInterpreter.Declare('btagSF{flavour} btagSF{flavour}_{shift} = btagSF{flavour}(\"{btagmaps}/{eff_map_year}/bTagEff_{eff_map_year}_ttbar_{bAlgo}_loose.root\", \"{year}\");')"], + 'expr': f'btagSF{flavour}_{shift}(CleanJet_pt, CleanJet_eta, CleanJet_jetIdx, nCleanJet, Jet_hadronFlavour, Jet_btag{bAlgo}, "{WP_eval}", "{shift}", "{tagger}","{eff_map_year}")', + 'samples' : mc, + } + +# Data/MC scale factors and systematic uncertainties +aliases['SFweight'] = { + 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF', 'btagSFbc', 'btagSFlight']), + 'samples': mc +} + +aliases['SFweightEleUp'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__Up', + 'samples': mc +} +aliases['SFweightEleDown'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__Down', + 'samples': mc +} +aliases['SFweightMuUp'] = { + 'expr': 'LepSF2l__mu_'+muWP+'__Up', + 'samples': mc +} +aliases['SFweightMuDown'] = { + 'expr': 'LepSF2l__mu_'+muWP+'__Down', + 'samples': mc +} diff --git a/ZpTreweighting/2022_v12/aliases_woBtagSF.py b/ZpTreweighting/2022_v12/aliases_woBtagSF.py new file mode 100644 index 00000000..4f882261 --- /dev/null +++ b/ZpTreweighting/2022_v12/aliases_woBtagSF.py @@ -0,0 +1,263 @@ +import os +import copy +import inspect + +configurations = os.path.realpath(inspect.getfile(inspect.currentframe())) # this file + +aliases = {} +aliases = OrderedDict() + +mc = [skey for skey in samples if skey not in ('Fake', 'DATA', 'Dyemb', 'DATA_EG', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] +# Commented out as not used (DS, 19Nov25) +# mc_emb = [skey for skey in samples if skey not in ('Fake', 'DATA', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] + +# Using LepSF2l__ele_cutBased_LooseID_tthMVA_Run3__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 from latest git repo push (https://github.com/latinos/PlotsConfigurationsRun3/blob/f8a0f50dfe6301543203d9d260ad721204a2739f/ControlRegions/DY/2022_v12/aliases.py#L14-L15) +eleWP = 'cutBased_LooseID_tthMVA_Run3' +muWP = 'cut_TightID_pfIsoTight_HWW_tthmva_67' + +aliases['LepWPCut'] = { + 'expr': 'LepCut2l__ele_'+eleWP+'__mu_'+muWP, + 'samples': mc + ['DATA'], +} + +aliases['LepWPSF'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__mu_'+muWP, + 'samples': mc +} + +# gen-matching to prompt only (GenLepMatch2l matches to *any* gen lepton) +aliases['PromptGenLepMatch2l'] = { + 'expr': 'Alt(Lepton_promptgenmatched, 0, 0) * Alt(Lepton_promptgenmatched, 1, 0)', + 'samples': mc +} + +aliases['gen_Zpt'] = { + # 'linesToAdd': [".L /afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+"], + # 'linesToAdd': ['.L /eos/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+'], + 'linesToAdd': [ + """ +#ifndef getGenZpt +#define getGenZpt + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" + +using namespace ROOT; +using namespace ROOT::VecOps; + +double GetGenZpt( + int nGenPart, + RVecF GenPart_pt, + RVecI GenPart_pdgId, + RVecI GenPart_genPartIdxMother, + RVecI GenPart_statusFlags, + float gen_ptll + ){ + + + + // Find Gen pT of Z decaying into leptons + unsigned nGen = nGenPart; + std::vector LepCands{}; + std::vector MotherIdx{}; + std::vector MotherPdgId{}; + int pdgId, sFlag, MIdx; + bool hasZ = false; + //std::cout << "==========" << std::endl; + for (unsigned iGen{0}; iGen != nGen; ++iGen){ + pdgId = std::abs(GenPart_pdgId[iGen]); + sFlag = GenPart_statusFlags[iGen]; + //std::cout << pdgId << " ; " << sFlag << " ; " << GenPart_pt->At(iGen) << " ; " << GenPart_genPartIdxMother->At(iGen) << std::endl; + if (((pdgId == 11) || (pdgId == 13) || (pdgId == 15)) && ((sFlag >> 0 & 1) || (sFlag >> 2 & 1) || (sFlag >> 3 & 1) || (sFlag >> 4 & 1))){ + LepCands.push_back(iGen); + MIdx = GenPart_genPartIdxMother[iGen]; + MotherIdx.push_back(MIdx); + if (MIdx > -1){ + MotherPdgId.push_back(GenPart_pdgId[MIdx]); + if (GenPart_pdgId[MIdx]==23) hasZ = true; + }else{ + MotherPdgId.push_back(0); + } + } + } + + //std::cout << "Check:" << std::endl; + for (unsigned iGen{0}; iGen != LepCands.size(); ++iGen){ + for (unsigned jGen{0}; jGen != LepCands.size(); ++jGen){ + if (jGen <= iGen) continue; + //std::cout << iGen << " ; " << MotherIdx[iGen] << " ; " << jGen << " ; " << MotherIdx[jGen] << " ; " << MotherPdgId[iGen] << " ; " << hasZ << std::endl; + // Some DY samples generate the Z; others have the two leptons produced directly -> motherId is 0 for those events + if (hasZ){ + if (MotherIdx[iGen] == MotherIdx[jGen] && MotherPdgId[iGen] == 23) return GenPart_pt[MotherIdx[iGen]]; + }else{ + if (MotherIdx[iGen] == MotherIdx[jGen] && MotherIdx[iGen] == 0) return GenPart_pt[MotherIdx[iGen]]; + } + } + } + //std::cout << "Falling back!" << std::endl; + return gen_ptll; + +} + +#endif + """], + 'class': 'GetGenZpt', + 'args': 'nGenPart, GenPart_pt, GenPart_pdgId, GenPart_genPartIdxMother, GenPart_statusFlags, gen_ptll', + # 'expr': 'gen_ptll', + 'samples': mc +} + +# Jet bins +# using Alt(CleanJet_pt, n, 0) instead of Sum(CleanJet_pt >= 30) because jet pt ordering is not strictly followed in JES-varied samples + +# No jet with pt > 30 GeV +aliases['zeroJet'] = { + 'expr': 'Alt(CleanJet_pt, 0, 0) < 30.' +} + +aliases['oneJet'] = { + 'expr': 'Alt(CleanJet_pt, 0, 0) > 30.' +} + +aliases['multiJet'] = { + 'expr': 'Alt(CleanJet_pt, 1, 0) > 30.' +} + +aliases['noJetInHorn'] = { + 'expr' : 'Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', +} + +######################################################################## +# B-Tagging WP: https://btv-wiki.docs.cern.ch/ScaleFactors/Run3Summer22/ +######################################################################## + +# Algo / WP / WP cut +btagging_WPs = { + "DeepFlavB" : { + "loose" : "0.0583", + "medium" : "0.3086", + "tight" : "0.7183", + "xtight" : "0.8111", + "xxtight" : "0.9512", + }, + "RobustParTAK4B" : { + "loose" : "0.0849", + "medium" : "0.4319", + "tight" : "0.8482", + "xtight" : "0.9151", + "xxtight" : "0.9874", + }, + "PNetB" : { + "loose" : "0.0470", + "medium" : "0.2450", + "tight" : "0.6734", + "xtight" : "0.7862", + "xxtight" : "0.9610", + } +} + +# Algo / SF name +btagging_SFs = { + "DeepFlavB" : "deepjet", + "RobustParTAK4B" : "partTransformer", + "PNetB" : "partNet", +} + +# Algorithm and WP selection +bAlgo = 'PNetB' # ['DeepFlavB','RobustParTAK4B','PNetB'] +WP = 'loose' # ['loose','medium','tight','xtight','xxtight'] + +# Access information from dictionaries +bWP = btagging_WPs[bAlgo][WP] +bSF = btagging_SFs[bAlgo] + +# # B tagging selections and scale factors +aliases['bVeto'] = { + 'expr': f'Sum(CleanJet_pt > 20. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) == 0' +} + +aliases['bReq'] = { + 'expr': f'Sum(CleanJet_pt > 30. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) >= 1' +} + +# Commenting out, as this was not included in latest git repo (https://github.com/latinos/PlotsConfigurationsRun3/blob/f8a0f50dfe6301543203d9d260ad721204a2739f/ControlRegions/DY/2022_v12/aliases.py#L89-L104) +# aliases['bVetoSF'] = { +# 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>20 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<20 || abs(CleanJet_eta)>2.5))))'.format(bSF), +# 'samples': mc +# } + +# aliases['bReqSF'] = { +# 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>30 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<30 || abs(CleanJet_eta)>2.5))))'.format(bSF), +# 'samples': mc +# } + +# # Top control region +# aliases['topcr'] = { +# 'expr': 'mtw2>30 && mll>50 && ((zeroJet && !bVeto) || bReq)' +# } + +# # WW control region +# aliases['wwcr'] = { +# 'expr': 'mth>60 && mtw2>30 && mll>100 && bVeto' +# } + +# # Overall b tag SF +# aliases['btagSF'] = { +# 'expr': '(bVeto || (topcr && zeroJet))*bVetoSF + (topcr && !zeroJet)*bReqSF', +# 'samples': mc +# } + +# # Systematic uncertainty variations +# for shift in ['jes','lf','hf','lfstats1','lfstats2','hfstats1','hfstats2','cferr1','cferr2']: + +# for targ in ['bVeto', 'bReq']: +# alias = aliases['%sSF%sup' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) +# alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_up_%s' % shift) + +# alias = aliases['%sSF%sdown' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) +# alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_down_%s' % shift) + +# aliases['btagSF%sup' % shift] = { +# 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'up'), +# 'samples': mc +# } + +# aliases['btagSF%sdown' % shift] = { +# 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'down'), +# 'samples': mc +# } + +########################################################################## +# End of b tagging +########################################################################## + +# Data/MC scale factors and systematic uncertainties +aliases['SFweight'] = { + # 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF','btagSF']), + 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF']), + 'samples': mc +} + +aliases['SFweightEleUp'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__Up', + 'samples': mc +} +aliases['SFweightEleDown'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__Down', + 'samples': mc +} +aliases['SFweightMuUp'] = { + 'expr': 'LepSF2l__mu_'+muWP+'__Up', + 'samples': mc +} +aliases['SFweightMuDown'] = { + 'expr': 'LepSF2l__mu_'+muWP+'__Down', + 'samples': mc +} diff --git a/ZpTreweighting/2022_v12/automate.py b/ZpTreweighting/2022_v12/automate.py new file mode 100644 index 00000000..76e49d4d --- /dev/null +++ b/ZpTreweighting/2022_v12/automate.py @@ -0,0 +1,744 @@ +#!/usr/bin/env python3 +""" +run_ZpTrw_workflow.py +===================== +Automated Z pT reweighting extraction workflow. + +The script orchestrates the following steps: + + Pre-Phase 1 — prepare configuration files + ------------------------------------------ + 0a. Patch configuration.py: set tag = "{year}_{sample_type}_{original_tag}" + 0b. Comment out the addSampleWeight line for DY pT reweighting in samples.py + (weights cannot be applied before they are derived) + + Round 1 — ZpTreweighting analysis + ---------------------------------- + 1a. mkShapesRDF -c 1 compile configuration + 1b. mkShapesRDF -o 0 -f . -b 1 submit HTCondor jobs + 1c. (wait) poll condor until all jobs finish + 1d. mkShapesRDF -o 2 -f . merge individual ROOT files (hadd) + 2. extract_Zptrw.py extract the Z pT reweighting function + and overwrite dyZpTrw.json + 2b. Move extract_Zptrw.py plots to plots_{year}_{sample_type}_obtainWeights/ + 2c. mkPlot --onlyPlot cratio create comparison plots on the merged file + + Round 2 — main analysis (optional, --second-analysis DIR) + ---------------------------------------------------------- + 3a. Update DYrew key in aliases.py with the correct year/sample-type + 3b. Uncomment addSampleWeight for DY pT reweighting in samples.py + 3c. mkShapesRDF -c 1 compile second analysis + 3d. mkShapesRDF -o 0 -f . -b 1 submit second-round condor jobs + 3e. (wait) poll condor until all jobs finish + 3f. mkShapesRDF -o 2 -f . merge second-round ROOT files + 3g. mkPlot --onlyPlot cratio create comparison plots for second round + +Prerequisites +------------- + * Source mkShapesRDF setup first + +Typical usage +------------- + # Run from inside ZpTreweighting/ or give the folder explicitly: + python automate.py --year 2022 --sample-type LO + + # Also kick off the second-round analysis immediately after: + python automate.py --year 2022 --sample-type LO --second-analysis ./ +""" + +import argparse +import glob +import os +import re +import shutil +import subprocess +import sys +import time + + +def banner(msg): + width = max(60, len(msg) + 4) + print("\n" + "=" * width) + print(f" {msg}") + print("=" * width) + + +def info(msg): + print(f" {msg}") + +# Run commands +def run_cmd(cmd, dry_run=False, cwd=None): + """Print and (optionally) execute *cmd*. + + *cmd* may be a list of strings or a single shell string. + Returns the process exit code (always 0 in dry-run mode). + """ + display = " ".join(cmd) if isinstance(cmd, list) else cmd + info(f"$ {display}") + if dry_run: + return 0 + result = subprocess.run(cmd, cwd=cwd, shell=isinstance(cmd, str)) + return result.returncode + + +def run_cmd_output(cmd, cwd=None): + """Run *cmd* and return *(returncode, stdout, stderr)* as strings.""" + result = subprocess.run( + cmd, + cwd=cwd, + capture_output=True, + text=True, + shell=isinstance(cmd, str), + ) + return result.returncode, result.stdout, result.stderr + + +# HTCondor job tracking +# Return the set of condor cluster IDs found in the job log files. Log files are expected at ``{batch_dir}/{tag}/**/log.txt`` +def _get_cluster_ids_from_logs(batch_dir, tag): + log_pattern = os.path.join(batch_dir, tag, "**", "log.txt") + log_files = glob.glob(log_pattern, recursive=True) + cluster_ids = set() + for log_file in log_files: + try: + with open(log_file) as fh: + for line in fh: + m = re.match(r"000 \((\d+)\.\d+\.\d+\)", line) + if m: + cluster_ids.add(m.group(1)) + except OSError: + pass + return cluster_ids + +# Return the number of jobs still in the condor queue for *cluster_id*. +def _count_condor_jobs_in_cluster(cluster_id): + rc, stdout, _ = run_cmd_output(["condor_q", str(cluster_id)]) + if rc != 0: + # cluster is gone (all jobs completed or removed) + return 0 + m = re.search(r"(\d+) jobs?", stdout) + return int(m.group(1)) if m else 0 + +# Hold until all HTCondor jobs for *tag* are no longer queued. +def wait_for_condor_jobs(batch_dir, tag, poll_interval=120, dry_run=False): + CONDOR_REGISTRATION_DELAY = 10 + + if dry_run: + info("[dry-run] Skipping condor wait.") + return + + # Give HTCondor a few seconds to register newly submitted jobs. + time.sleep(CONDOR_REGISTRATION_DELAY) + + cluster_ids = _get_cluster_ids_from_logs(batch_dir, tag) + if not cluster_ids: + info("WARNING: No condor cluster IDs found in log files. " + "Waiting 60 s and retrying once...") + time.sleep(60) + cluster_ids = _get_cluster_ids_from_logs(batch_dir, tag) + + if not cluster_ids: + info("WARNING: Still no cluster IDs found. " + "Assuming jobs have already completed or were submitted " + "outside HTCondor.") + return + + info(f"Tracking condor cluster(s): {', '.join(sorted(cluster_ids))}") + + while True: + remaining = { + cid for cid in cluster_ids + if _count_condor_jobs_in_cluster(cid) > 0 + } + if not remaining: + info("All condor jobs have completed.") + return + + total = sum(_count_condor_jobs_in_cluster(c) for c in remaining) + info( + f"[{time.strftime('%H:%M:%S')}] " + f"{total} job(s) still queued in " + f"cluster(s) {', '.join(sorted(remaining))}. " + f"Polling again in {poll_interval} s..." + ) + time.sleep(poll_interval) + + +# Configuration reader +# Execute *cfg_file* and return a dict with analysis settings. +def read_configuration(cfg_file): + ns = { + "__file__": os.path.abspath(cfg_file), + "os": os, + "sys": sys, + } + try: + with open(cfg_file) as fh: + exec(compile(fh.read(), cfg_file, "exec"), ns) + except Exception as exc: + # configuration.py may call os.getlogin() which can fail in some + # environments; fall through with whatever was captured so far. + info(f"WARNING: Error while parsing {cfg_file}: {exc}") + + tag = ns.get("tag", "ZpTreweighting") + output_folder = ns.get( + "outputFolder", + os.path.join( + "/eos/user", + os.environ.get("USER", "unknown")[0], + os.environ.get("USER", "unknown"), + "mkShapesRDF_rootfiles", + tag, + "rootFile", + ), + ) + return { + "tag": tag, + "outputFolder": output_folder.rstrip("/"), + "outputFile": ns.get("outputFile", f"mkShapes__{tag}.root"), + "batchFolder": ns.get("batchFolder", "condor"), + } + + +# Workflow phases +# Phase 1a+1b — compile and submit condor jobs. +def phase1_submit(zptrw_dir, dry_run=False): + banner("Phase 1a: Compile ZpTreweighting configuration") + rc = run_cmd(["mkShapesRDF", "-c", "1"], dry_run=dry_run, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: mkShapesRDF -c 1 failed (exit code {rc})") + + banner("Phase 1b: Submit ZpTreweighting condor jobs") + rc = run_cmd( + ["mkShapesRDF", "-o", "0", "-f", ".", "-b", "1"], + dry_run=dry_run, + cwd=zptrw_dir, + ) + if rc != 0: + sys.exit(f"ERROR: mkShapesRDF -o 0 failed (exit code {rc})") + + +# Phase 1c — wait for all condor jobs to finish. +def phase1_wait(zptrw_dir, cfg, poll_interval=120, dry_run=False): + banner("Phase 1c: Waiting for HTCondor jobs to complete") + batch_dir = os.path.join(zptrw_dir, cfg["batchFolder"]) + wait_for_condor_jobs( + batch_dir=batch_dir, + tag=cfg["tag"], + poll_interval=poll_interval, + dry_run=dry_run, + ) + + +# Phase 1d — merge individual job ROOT files (hadd). +def phase1_merge(zptrw_dir, dry_run=False): + banner("Phase 1d: Merge ROOT files") + rc = run_cmd( + ["mkShapesRDF", "-o", "2", "-f", "."], + dry_run=dry_run, + cwd=zptrw_dir, + ) + if rc != 0: + sys.exit(f"ERROR: Phase 1d - mkShapesRDF -o 2 (merge) failed (exit code {rc})") + + +# Configuration / samples file patching helpers + +# Append '{_suffix}' and prepend '{year}_{sample_type}_' to the tag variable in configuration.py. +def patch_configuration_tag(cfg_file, year, sample_type, suffix, dry_run=False): + banner("Patching configuration.py: prepending year/sample-type to tag") + + if not os.path.exists(cfg_file): + info(f"WARNING: {cfg_file} not found; skipping tag patch.") + return + + with open(cfg_file) as fh: + content = fh.read() + + prefix = f"{year}_{sample_type}_" + # Match: tag = "..." or tag = '...' (not already prefixed). + # Build pattern with f-string so the negative lookahead uses the actual prefix. + pattern = re.compile( + r"""^(\s*tag\s*=\s*)["'][^"']*["']""", + re.MULTILINE, + ) + + def _replace(m): + new_tag = f'{year}_{sample_type}_ZpTreweighting_{suffix}' + info(f" tag: → '{new_tag}'") + return f'{m.group(1)}"{new_tag}"' + + new_content, count = pattern.subn(_replace, content, count=1) + if count == 0: + info("WARNING: Could not find 'tag = ...' line in configuration.py; " + "skipping tag patch. Verify that configuration.py contains a " + "tag = \"\" assignment at module level.") + return + + if not dry_run: + with open(cfg_file, "w") as fh: + fh.write(new_content) + info(f" Updated: {cfg_file}") + else: + info("[dry-run] Would update configuration.py tag.") + + +# Comment out the addSampleWeight line for DY pT reweighting in samples.py. +def comment_addsampleweight_dy(samples_file, dry_run=False): + banner("Commenting out addSampleWeight for DY pT reweighting in samples.py") + + if not os.path.exists(samples_file): + info(f"WARNING: {samples_file} not found; skipping.") + return + + with open(samples_file) as fh: + content = fh.read() + + # Match an un-commented addSampleWeight call referencing a DY_*_ZpTrw weight. + # Pattern breakdown: + # ^(?![ \t]*#) — line must not start with optional whitespace + '#' + # ([ \t]*addSampleWeight — capture indentation + function name + # \s*\([^)]* — opening paren and any args + # ['"]DY_..._ZpTrw['"] — the DY ZpTrw weight argument + # [^)]*\)) — remaining args + closing paren + pattern = re.compile( + r"^(?![ \t]*#)([ \t]*addSampleWeight\s*\([^)]*['\"]DY_[A-Za-z0-9]+_ZpTrw['\"][^)]*\))", + re.MULTILINE, + ) + + if not pattern.search(content): + info("WARNING: No uncommented addSampleWeight DY ZpTrw line found; skipping.") + return + + new_content = pattern.sub(r"# \1", content) + info(" Commented out addSampleWeight DY ZpTrw line.") + + if not dry_run: + with open(samples_file, "w") as fh: + fh.write(new_content) + info(f" Updated: {samples_file}") + else: + info("[dry-run] Would comment out addSampleWeight DY ZpTrw line.") + +# Uncomment the addSampleWeight line for DY pT reweighting in samples.py. +def uncomment_addsampleweight_dy(samples_file, dry_run=False): + banner("Uncommenting addSampleWeight for DY pT reweighting in samples.py") + + if not os.path.exists(samples_file): + info(f"WARNING: {samples_file} not found; skipping.") + return + + with open(samples_file) as fh: + content = fh.read() + + # Match a commented addSampleWeight call referencing a DY_*_ZpTrw weight. + pattern = re.compile( + r"^([ \t]*)#[ \t]*(addSampleWeight\s*\([^)]*['\"]DY_[A-Za-z0-9]+_ZpTrw['\"][^)]*\))", + re.MULTILINE, + ) + + if not pattern.search(content): + info("WARNING: No commented addSampleWeight DY ZpTrw line found; skipping.") + return + + new_content = pattern.sub(r"\1\2", content) + info(" Uncommented addSampleWeight DY ZpTrw line.") + + if not dry_run: + with open(samples_file, "w") as fh: + fh.write(new_content) + info(f" Updated: {samples_file}") + else: + info("[dry-run] Would uncomment addSampleWeight DY ZpTrw line.") + +# Replace DYrew['old_year']['old_type'] with DYrew['{year}']['{sample_type}'] in aliases.py. +def update_aliases_dyrew_key(aliases_file, year, sample_type, dry_run=False): + banner(f"Updating DYrew key in {os.path.basename(aliases_file)}") + + if not os.path.exists(aliases_file): + info(f"WARNING: {aliases_file} not found; skipping DYrew key update.") + return + + with open(aliases_file) as fh: + content = fh.read() + + # Match DYrew['oldyear']['oldtype_jetbin'] or DYrew["oldyear"]["oldtype_jetbin"] + pattern = re.compile( + r"""DYrew\[\s*(['"])[^'"]+\1\s*\]\[\s*(['"])[^'"]+_(0j|1j|2j)\2\s*\]""" + ) + + def replacer(match): + quote1, quote2, jetbin = match.group(1), match.group(2), match.group(3) + return f"DYrew[{quote1}{year}{quote1}][{quote2}{sample_type}_{jetbin}{quote2}]" + + matches = pattern.findall(content) + if not matches: + info(f"No DYrew['...']['...'] references found in {aliases_file}; skipping.") + return + + new_content = pattern.sub(replacer, content) + info(f" Updated {len(matches)} DYrew key reference(s) → ['{year}']['{sample_type}_']") + + if not dry_run: + with open(aliases_file, "w") as fh: + fh.write(new_content) + info(f" Updated: {aliases_file}") + else: + info(f"[dry-run] Would update DYrew key in {aliases_file}.") + +# Plot helpers +# Move plots produced by extract_Zptrw.py into plots_{year}_{sample_type}_obtainWeights/. +def move_zptrw_plots(zptrw_dir, year, sample_type, folder_suffix="", dry_run=False): + banner("Moving extract_Zptrw.py plots to archive folder") + + target_dir = os.path.join(zptrw_dir, f"extractPlots_{year}_{sample_type}_{folder_suffix}") + plot_files = ( + glob.glob(os.path.join(zptrw_dir, "ZpTreweighting_*.pdf")) + + glob.glob(os.path.join(zptrw_dir, "ZpTreweighting_*.png")) + ) + + if not plot_files: + info("No ZpTreweighting_*.pdf/png files found to move.") + return + + info(f"Target folder: {target_dir}") + for src in plot_files: + dest = os.path.join(target_dir, os.path.basename(src)) + info(f" {os.path.basename(src)} → {os.path.relpath(dest, zptrw_dir)}") + if not dry_run: + os.makedirs(target_dir, exist_ok=True) + shutil.move(src, dest) + + if dry_run: + info("[dry-run] Would create target folder and move plot files.") + +# Run ``mkPlot --onlyPlot cratio --showIntegralLegend 1 --fileFormats png``. +def run_mkplot(analysis_dir, dry_run=False): + banner(f"Running mkPlot in {analysis_dir}") + rc = run_cmd( + ["mkPlot", "--onlyPlot", "cratio", "--showIntegralLegend", "1", + "--fileFormats", "png"], + dry_run=dry_run, + cwd=analysis_dir, + ) + if rc != 0: + info(f"WARNING: mkPlot exited with code {rc}. Continuing workflow.") + +# Phase 2 — run extract_Zptrw.py to derive weights and update dyZpTrw.json, or make the ratio plots after applying the weights. +def phase2_extract(zptrw_dir, cfg, year="2022", sample_type="LO", run_fit = "", dry_run=False): + banner("Phase 2: Extract Z pT reweighting function → update dyZpTrw.json") + + merged_file = os.path.join(cfg["outputFolder"], cfg["outputFile"]) + dyzptrw_json = os.path.join(zptrw_dir, "dyZpTrw.json") + extract_script = os.path.join(zptrw_dir, "extract_Zptrw.py") + + if not dry_run and not os.path.exists(merged_file): + sys.exit( + f"ERROR: Merged ROOT file not found:\n" + f" {merged_file}\n" + f"Run 'mkShapesRDF -o 2 -f .' in {zptrw_dir} first." + ) + for njet in [0, 1, 2]: + # derive weights in Z->MuMu channel, 0 jet bin + if run_fit == "-f": + # define normalization when fitting is requested, else, just plot + cmd = [sys.executable, extract_script, "-f", "-n", "2", "-c", "mm", "-nj", str(njet), "--input", merged_file, "--write-json", dyzptrw_json, "--year", year, "--sample-type", sample_type] + else: + cmd = [sys.executable, extract_script, "-c", "mm", "-nj", str(njet), "--input", merged_file, "--write-json", dyzptrw_json, "--year", year, "--sample-type", sample_type] + rc = run_cmd(cmd, dry_run=dry_run, cwd=zptrw_dir) + # Make plots in Z->ee channel, 0 jet bin + cmd = [sys.executable, extract_script, "-c", "ee", "-nj", str(njet), "--input", merged_file] + rc = run_cmd(cmd, dry_run=dry_run, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: extract_Zptrw.py failed (exit code {rc})") + info(f"dyZpTrw.json updated for nJet={njet} → {dyzptrw_json}") + +# Phase 3 — prepare, compile, submit, wait, merge, and plot second-round analysis. +def phase3_second_round(second_dir, year, sample_type, poll_interval=120, + skip_second_wait=False, skip_second_merge=False, + dry_run=False): + banner(f"Phase 3: Second-round mkShapesRDF in\n {second_dir}") + + # Update aliases.py and samples.py before compiling + aliases_file = os.path.join(second_dir, "aliases.py") + update_aliases_dyrew_key(aliases_file, year, sample_type, dry_run=dry_run) + + samples_file = os.path.join(second_dir, "samples.py") + uncomment_addsampleweight_dy(samples_file, dry_run=dry_run) + + rc = run_cmd(["mkShapesRDF", "-c", "1"], dry_run=dry_run, cwd=second_dir) + if rc != 0: + sys.exit( + f"ERROR: mkShapesRDF -c 1 failed in {second_dir} (exit code {rc})" + ) + + rc = run_cmd( + ["mkShapesRDF", "-o", "0", "-f", ".", "-b", "1"], + dry_run=dry_run, + cwd=second_dir, + ) + if rc != 0: + sys.exit( + f"ERROR: mkShapesRDF -o 0 failed in {second_dir} (exit code {rc})" + ) + + # Wait for second-round condor jobs + second_cfg_file = os.path.join(second_dir, "configuration.py") + has_cfg = os.path.exists(second_cfg_file) + + if not skip_second_wait: + if has_cfg or dry_run: + banner("Phase 3c: Waiting for second-round HTCondor jobs to complete") + second_cfg = ( + read_configuration(second_cfg_file) + if has_cfg + else {"tag": "unknown", "batchFolder": "condor", + "outputFolder": ".", "outputFile": "output.root"} + ) + batch_dir = os.path.join(second_dir, second_cfg["batchFolder"]) + wait_for_condor_jobs( + batch_dir=batch_dir, + tag=second_cfg["tag"], + poll_interval=poll_interval, + dry_run=dry_run, + ) + else: + info(f"WARNING: No configuration.py found in {second_dir}; " + "skipping second-round condor wait.") + else: + info("\n[skip-second-wait] Skipping second-round condor wait.") + + # Merge second-round ROOT files + if not skip_second_merge: + banner("Phase 3d: Merge second-round ROOT files") + rc = run_cmd( + ["mkShapesRDF", "-o", "2", "-f", "."], + dry_run=dry_run, + cwd=second_dir, + ) + if rc != 0: + sys.exit( + f"ERROR: mkShapesRDF -o 2 (merge) failed in {second_dir} " + f"(exit code {rc})" + ) + else: + info("\n[skip-second-merge] Skipping second-round ROOT file merge.") + + # Create RDF plots for the second-round merged output + run_mkplot(second_dir, dry_run=dry_run) + + +# CLI +def parse_args(): + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + # ---- paths ---- + parser.add_argument( + "--zptrw-folder", + default=".", + metavar="DIR", + help="Path to the ZpTreweighting analysis folder " + "(default: current directory).", + ) + parser.add_argument( + "--second-analysis", + default=None, + metavar="DIR", + help="Path to the second-round analysis folder. " + "When given, Phase 3 updates aliases/samples, compiles, submits, " + "waits, merges, and runs mkPlot there after dyZpTrw.py has been updated.", + ) + + # ---- physics ---- + parser.add_argument( + "--year", + default="2022", + help="Year key written to DYrew in dyZpTrw.json (default: '2022').", + ) + parser.add_argument( + "--sample-type", + default="LO", + help="Sample-type key written to DYrew in dyZpTrw.json " + "(default: 'LO').", + ) + + # ---- condor polling ---- + parser.add_argument( + "--poll-interval", + type=int, + default=120, + metavar="SECONDS", + help="Seconds between condor_q polls while waiting for jobs " + "(default: 120).", + ) + + # ---- skip flags ---- + skip = parser.add_argument_group("skip flags (for re-running partial workflow)") + skip.add_argument( + "--skip-submit", + action="store_true", + help="Skip Phase 1a+1b (assume jobs are already running or done).", + ) + skip.add_argument( + "--skip-wait", + action="store_true", + help="Skip Phase 1c (assume all jobs have already finished).", + ) + skip.add_argument( + "--skip-merge", + action="store_true", + help="Skip Phase 1d (assume the merged ROOT file already exists).", + ) + skip.add_argument( + "--skip-extract", + action="store_true", + help="Skip Phase 2 (assume dyZpTrw.json is already up to date).", + ) + skip.add_argument( + "--skip-second-wait", + action="store_true", + help="Skip waiting for second-round HTCondor jobs (Phase 3c).", + ) + skip.add_argument( + "--skip-second-merge", + action="store_true", + help="Skip merging second-round ROOT files (Phase 3d).", + ) + + # ---- misc ---- + parser.add_argument( + "--dry-run", + action="store_true", + help="Print every command that would be run without executing it.", + ) + + return parser.parse_args() + + +def main(): + args = parse_args() + + zptrw_dir = os.path.abspath(args.zptrw_folder) + if not os.path.isdir(zptrw_dir): + sys.exit(f"ERROR: ZpTreweighting folder not found: {zptrw_dir}") + + cfg_file = os.path.join(zptrw_dir, "configuration.py") + if not os.path.exists(cfg_file): + sys.exit(f"ERROR: configuration.py not found in {zptrw_dir}") + + banner("Z pT reweighting workflow") + info(f"ZpTreweighting folder : {zptrw_dir}") + if args.dry_run: + info("*** DRY-RUN mode — no commands will be executed ***") + + # ---- Pre-Phase 1: Patch configuration.py tag and comment out DY pT rw weight ---- + patch_configuration_tag(cfg_file, args.year, args.sample_type, suffix="obtainWeights", dry_run=args.dry_run) + + cfg = read_configuration(cfg_file) + info(f"tag : {cfg['tag']}") + info(f"outputFolder : {cfg['outputFolder']}") + info(f"outputFile : {cfg['outputFile']}") + info(f"batchFolder : {cfg['batchFolder']}") + + samples_file = os.path.join(zptrw_dir, "samples.py") + comment_addsampleweight_dy(samples_file, dry_run=args.dry_run) + + # ---- Phase 1a+1b: Submit ---- + if not args.skip_submit: + phase1_submit(zptrw_dir, dry_run=args.dry_run) + else: + info("\n[skip-submit] Skipping job submission.") + + # ---- Phase 1c: Wait ---- + if not args.skip_wait and not args.skip_submit: + phase1_wait( + zptrw_dir, + cfg, + poll_interval=args.poll_interval, + dry_run=args.dry_run, + ) + elif args.skip_wait: + info("\n[skip-wait] Skipping condor wait.") + + # ---- Phase 1d: Merge ---- + if not args.skip_merge: + phase1_merge(zptrw_dir, dry_run=args.dry_run) + else: + info("\n[skip-merge] Skipping ROOT file merge.") + + # ---- Phase 2: Extract + Update ---- + if not args.skip_extract: + phase2_extract(zptrw_dir, cfg, year=args.year, sample_type=args.sample_type, run_fit="-f", dry_run=args.dry_run) + # Move plots produced by extract_Zptrw.py to archive folder + move_zptrw_plots(zptrw_dir, args.year, args.sample_type, folder_suffix = "obtainWeights", dry_run=args.dry_run) + # Create RDF plots from the merged ROOT file + run_mkplot(zptrw_dir, dry_run=args.dry_run) + # Rename condor and config folders: + cmd = ["mv", os.path.join(zptrw_dir, "condor/"), os.path.join(zptrw_dir, f"condor_{args.year}_{args.sample_type}_obtainWeights")] + rc = run_cmd(cmd, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: failed to rename condor directory (exit code {rc})") + cmd = ["pwd"] + rc = run_cmd(cmd, cwd=zptrw_dir) + cmd = ["mv", os.path.join(zptrw_dir, "configs/"), os.path.join(zptrw_dir, f"configs_{args.year}_{args.sample_type}_obtainWeights")] + rc = run_cmd(cmd, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: failed to rename configs directory (exit code {rc})") + + else: + info("\n[skip-extract] Skipping weight extraction, plot archiving, and mkPlot " + "(assumes dyZpTrw.json and plots are already up to date).") + + # ---- Phase 3 (optional): Second analysis ---- + if args.second_analysis: + second_dir = os.path.abspath(args.second_analysis) + if not os.path.isdir(second_dir): + sys.exit(f"ERROR: Second-analysis folder not found: {second_dir}") + + patch_configuration_tag(cfg_file, args.year, args.sample_type, suffix="afterReweighting", dry_run=args.dry_run) + cfg = read_configuration(cfg_file) + info(f"tag : {cfg['tag']}") + info(f"outputFolder : {cfg['outputFolder']}") + info(f"outputFile : {cfg['outputFile']}") + info(f"batchFolder : {cfg['batchFolder']}") + + phase3_second_round( + second_dir, + year=args.year, + sample_type=args.sample_type, + poll_interval=args.poll_interval, + skip_second_wait=args.skip_second_wait, + skip_second_merge=args.skip_second_merge, + dry_run=args.dry_run, + ) + phase2_extract(zptrw_dir, cfg, year=args.year, sample_type=args.sample_type, run_fit="", dry_run=args.dry_run) + # Move plots produced by extract_Zptrw.py to folder + move_zptrw_plots(zptrw_dir, args.year, args.sample_type, folder_suffix = "afterReweighting", dry_run=args.dry_run) + # Create comparison plots from the merged ROOT file + run_mkplot(zptrw_dir, dry_run=args.dry_run) + # Rename log, condor, and config folders: + cmd = ["mv", os.path.join(zptrw_dir, "condor/"), os.path.join(zptrw_dir, f"condor_{args.year}_{args.sample_type}_afterReweighting")] + rc = run_cmd(cmd, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: failed to rename condor directory (exit code {rc})") + cmd = ["mv", os.path.join(zptrw_dir, "configs/"), os.path.join(zptrw_dir, f"configs_{args.year}_{args.sample_type}_afterReweighting")] + rc = run_cmd(cmd, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: failed to rename configs directory (exit code {rc})") + + banner("Workflow complete!") + if args.second_analysis: + info("Second-round analysis finished. Plots are in the analysis folder.") + else: + info("dyZpTrw.json has been updated.") + info("To run the second-round analysis:") + info(" cd ") + info(" mkShapesRDF -c 1") + info(" mkShapesRDF -o 0 -f . -b 1") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ZpTreweighting/configuration.py b/ZpTreweighting/2022_v12/configuration.py similarity index 100% rename from ZpTreweighting/configuration.py rename to ZpTreweighting/2022_v12/configuration.py diff --git a/ZpTreweighting/cuts.py b/ZpTreweighting/2022_v12/cuts.py similarity index 94% rename from ZpTreweighting/cuts.py rename to ZpTreweighting/2022_v12/cuts.py index 35f4df1e..41a5ca00 100644 --- a/ZpTreweighting/cuts.py +++ b/ZpTreweighting/2022_v12/cuts.py @@ -8,8 +8,9 @@ && abs(Lepton_eta[1]) < 2.5 \ && mll > 60 \ && mll < 120 \ - && zeroJet \ ' +# Remove zeroJet preselection as weights are being derived in nJet bins (DS, 18Mar26) + # && zeroJet \ # Individual cuts and categories diff --git a/ZpTreweighting/2022_v12/dyZpTrw.json b/ZpTreweighting/2022_v12/dyZpTrw.json new file mode 100644 index 00000000..3a247a9b --- /dev/null +++ b/ZpTreweighting/2022_v12/dyZpTrw.json @@ -0,0 +1,17 @@ +{ + "2022": { + "NLO_0j": "0.843196*(0.211634*TMath::Erf((x-9.120552)/3.728196)-0.024628*x+0.000400*TMath::Sq(x)+1.361984)", + "NLO_1j": "0.843196*(0.211634*TMath::Erf((x-9.120552)/3.728196)-0.024628*x+0.000400*TMath::Sq(x)+1.361984)", + "NLO_2j": "0.843196*(0.211634*TMath::Erf((x-9.120552)/3.728196)-0.024628*x+0.000400*TMath::Sq(x)+1.361984)" + }, + "2023": { + "NLO_0j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)", + "NLO_1j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)", + "NLO_2j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)" + }, + "2024": { + "NLO_0j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)", + "NLO_1j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)", + "NLO_2j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)" + } +} \ No newline at end of file diff --git a/ZpTreweighting/extract_Zptrw.py b/ZpTreweighting/2022_v12/extract_Zptrw.py similarity index 67% rename from ZpTreweighting/extract_Zptrw.py rename to ZpTreweighting/2022_v12/extract_Zptrw.py index c81805b5..ef8bff4c 100644 --- a/ZpTreweighting/extract_Zptrw.py +++ b/ZpTreweighting/2022_v12/extract_Zptrw.py @@ -15,34 +15,21 @@ style["font.size"] = 18 plt.style.use(style) -def calc_norm_factor(dy_hist, fitting_function, fit_params): - numerator = 0.0 - denominator = 0.0 - first_bin = dy_hist.FindBin(0) - last_bin = dy_hist.FindBin(50) - print("\nNOTE: Using fit function (", fitting_function,") to calculate normalization factor.\n") # [0]*TMath::Erf((x-[1])/[2]) + [3]*x + [4]*x**2 + [5] - for bin_idx in range(first_bin, last_bin + 1): - mc_events = dy_hist.GetBinContent(bin_idx) - # weight = histo_ratio.GetBinContent(bin_idx) - binCenter = dy_hist.GetXaxis().GetBinCenter(bin_idx) - weight = fit_params[0]*ROOT.TMath.Erf((binCenter - fit_params[1])/fit_params[2]) + fit_params[3]*binCenter + fit_params[4]*binCenter**2 + fit_params[5] - numerator += mc_events - denominator += mc_events * weight - norm_factor = numerator / denominator if denominator != 0 else 1.0 - print("Normalization factor:", norm_factor) - return norm_factor - parser = argparse.ArgumentParser(description='Extract data and fit with Gaussian.') parser.add_argument('-f', action='store_true', help='Fit the ratio plot using Erf.') parser.add_argument('-n', type=int, default=0, help='Normalization method:\n1: Ratio of integral of MC over weights*MC.\n2: Normalize MC to data integral before calculating rw factor.') +parser.add_argument('-c', type=str, default="mm", help='Z decay channel.') +parser.add_argument('-nj', type=int, default=0, help='Jet bin category(number of jet bins).') parser.add_argument('-i', '--input', default='mkShapes__ZpTreweighting.root', help='Path to the merged ROOT file (default: mkShapes__ZpTreweighting.root)') parser.add_argument('--write-json', default=None, help='If given, write the updated dyZpTrw.json to this path after a successful fit (requires -f). The file is overwritten.') parser.add_argument('--year', default='2022', help="Year key in the DYrew dict written to dyZpTrw.json (default: '2022')") parser.add_argument('--sample-type', default='LO', help="Sample-type key in the DYrew dict written to dyZpTrw.json (default: 'LO')") +parser.add_argument('--plot-xrange', type=float, default=80, help='maximum X-axis range for the plots (default: 80)') +parser.add_argument('--fit-xrange', type=float, default=50, help='maximum X-axis range for the fits (default: 50)') args = parser.parse_args() root_file = ROOT.TFile(args.input) -channel = "Zmm_0j" +channel = f"Z{args.c}_{args.nj}j" zee_dir = root_file.Get(channel) ptll_dir = zee_dir.Get("ptll") @@ -58,27 +45,47 @@ def calc_norm_factor(dy_hist, fitting_function, fit_params): histo_trueData.Add(histo_diboson, -1) histo_trueData.Add(histo_SMhiggs, -1) +plot_range = [0, args.plot_xrange] +fit_range = [0, args.fit_xrange] + +def calc_norm_factor(dy_hist, fitting_function, fit_params): + numerator = 0.0 + denominator = 0.0 + first_bin = dy_hist.FindBin(fit_range[0]) + last_bin = dy_hist.FindBin(fit_range[1]) + print("\nNOTE: Using fit function (", fitting_function,") to calculate normalization factor.\n") # [0]*TMath::Erf((x-[1])/[2]) + [3]*x + [4]*x**2 + [5] + for bin_idx in range(first_bin, last_bin + 1): + mc_events = dy_hist.GetBinContent(bin_idx) + # weight = histo_ratio.GetBinContent(bin_idx) + binCenter = dy_hist.GetXaxis().GetBinCenter(bin_idx) + weight = fit_params[0]*ROOT.TMath.Erf((binCenter - fit_params[1])/fit_params[2]) + fit_params[3]*binCenter + fit_params[4]*binCenter**2 + fit_params[5] + numerator += mc_events + denominator += mc_events * weight + norm_factor = numerator / denominator if denominator != 0 else 1.0 + print("Normalization factor:", norm_factor) + return norm_factor + # # Rebin hists # histo_trueData.Rebin(4) # histo_DY.Rebin(4) -# Calculate the integral/sum of histo_DY and histo_ratio for x axis in [0, 50) -integral_histo_DY = histo_DY.Integral(histo_DY.FindBin(0), histo_DY.FindBin(50) - 1) -integral_histo_DATA = histo_trueData.Integral(histo_trueData.FindBin(0), histo_trueData.FindBin(50) - 1) +# Calculate the integral/sum of histo_DY and histo_ratio for x axis in [0, fit_range[1]) +integral_histo_DY = histo_DY.Integral(histo_DY.FindBin(fit_range[0]), histo_DY.FindBin(fit_range[1]) - 1) +integral_histo_DATA = histo_trueData.Integral(histo_trueData.FindBin(fit_range[0]), histo_trueData.FindBin(fit_range[1]) - 1) norm_factor2 = integral_histo_DATA/integral_histo_DY print("Normalization factor 2:", norm_factor2) if args.n == 2: histo_DY.Scale(norm_factor2) - integral_histo_DYscaled = histo_DY.Integral(histo_DY.FindBin(0), histo_DY.FindBin(50) - 1) + integral_histo_DYscaled = histo_DY.Integral(histo_DY.FindBin(fit_range[0]), histo_DY.FindBin(fit_range[1]) - 1) # Create a ratio plot of DATA to DY histo_ratio = histo_trueData.Clone("histo_ratio") histo_ratio.Divide(histo_DY) -integral_histo_ratio = histo_ratio.Integral(histo_ratio.FindBin(0), histo_ratio.FindBin(50) - 1) +integral_histo_ratio = histo_ratio.Integral(histo_ratio.FindBin(fit_range[0]), histo_ratio.FindBin(fit_range[1]) - 1) # fitting_functions = ["[0]*x**6 + [1]*x**5 + [2]*x**4 + [3]*x**3 + [4]*x**2 + [5]*x + [6]", "[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x**2)"] -fitting_functions = ["([0]*TMath::Erf((x-[1])/[2]) + [3]*x + [4]*x**2 + [5])"] +fitting_functions = ["([0]*TMath::Erf((x-[1])/[2]) + [3]*x + [4]*TMath::Sq(x) + [5])"] initial_guesses = [[0.0, 5.0, 10.0, 0.0, 0.0, 1.0]] save_name_suffixes = [channel] for fitfunc, initguess, savename in zip(fitting_functions, initial_guesses, save_name_suffixes): @@ -97,7 +104,7 @@ def calc_norm_factor(dy_hist, fitting_function, fit_params): histo_DY.GetYaxis().SetTitleSize(0.06) histo_DY.GetYaxis().SetTitleOffset(0.8) histo_DY.GetYaxis().SetLabelSize(0.05) - histo_DY.GetXaxis().SetRangeUser(0, 80) + histo_DY.GetXaxis().SetRangeUser(plot_range[0], plot_range[1]) # Draw DY as reference, then data points # histo_DY.SetLineColor(ROOT.kBlue) histo_DY.SetStats(False) @@ -107,7 +114,7 @@ def calc_norm_factor(dy_hist, fitting_function, fit_params): histo_trueData.SetMarkerStyle(8) histo_trueData.SetMarkerColor(ROOT.kBlack) histo_trueData.SetMarkerSize(0.8) - histo_trueData.GetXaxis().SetRangeUser(0, 80) + histo_trueData.GetXaxis().SetRangeUser(plot_range[0], plot_range[1]) histo_trueData.Draw("E1 SAME") # CMS label @@ -116,10 +123,10 @@ def calc_norm_factor(dy_hist, fitting_function, fit_params): label.SetTextSize(0.040) label.DrawLatex(0.12, 0.92, "#bf{CMS} #it{Preliminary}") label.DrawLatex(0.55, 0.92, "L = 8.2 fb^{-1} (#sqrt{s} = 13.6 TeV)") - label.DrawLatex(0.15, 0.2, f"num(DY) events in (0,50) GeV = {integral_histo_DY:.3f}") - label.DrawLatex(0.15, 0.15, f"num(DATA) events in (0,50) GeV = {integral_histo_DATA:.3f}") + label.DrawLatex(0.15, 0.2, f"num(DY) events in ({fit_range[0]},{fit_range[1]}) GeV = {integral_histo_DY:.3f}") + label.DrawLatex(0.15, 0.15, f"num(DATA) events in ({fit_range[0]},{fit_range[1]}) GeV = {integral_histo_DATA:.3f}") if args.n == 2: - label.DrawLatex(0.15, 0.1, f"num(DY normalized) events in (0,50) GeV = {integral_histo_DYscaled:.3f}") + label.DrawLatex(0.15, 0.1, f"num(DY normalized) events in ({fit_range[0],fit_range[1]}) GeV = {integral_histo_DYscaled:.3f}") leg = ROOT.TLegend(0.60, 0.70, 0.88, 0.88) @@ -150,7 +157,7 @@ def calc_norm_factor(dy_hist, fitting_function, fit_params): h_ratio_points.GetXaxis().SetTitle("p_{T}^{ll} [GeV]") h_ratio_points.GetXaxis().SetTitleSize(0.12) h_ratio_points.GetXaxis().SetLabelSize(0.10) - h_ratio_points.GetXaxis().SetRangeUser(0, 80) + h_ratio_points.GetXaxis().SetRangeUser(plot_range[0], plot_range[1]) h_ratio_points.SetMinimum(0.0) h_ratio_points.SetMaximum(2.0) @@ -169,8 +176,8 @@ def calc_norm_factor(dy_hist, fitting_function, fit_params): # erf(x; p0, p1, p2, p3) = p0 + p1 * TMath::Erf((x - p2)/p3) fit_func = ROOT.TF1("fit_erf", fitfunc, - histo_ratio.GetXaxis().GetXmin(), - 80)#histo_ratio.GetXaxis().GetXmax()) + fit_range[0],#histo_ratio.GetXaxis().GetXmin(), + fit_range[1])#histo_ratio.GetXaxis().GetXmax()) fit_func.SetParameters(*initguess) # reasonable starting values fit_func.SetLineColor(ROOT.kRed) @@ -194,9 +201,16 @@ def calc_norm_factor(dy_hist, fitting_function, fit_params): else: print("Fit failed or invalid - cannot compute Chi2/NDF") - fit_func.Draw("SAME") + # Plot only until 50 GeV, the value after 50 GeV will be the fit function's value at 50 GeV + fit_func.Draw("SAME") + const_val = fit_func.Eval(fit_range[1]) + const_func = ROOT.TF1("const_func", f"{const_val}", fit_range[1], plot_range[1]) # NOTE - assuming plot_range[1] is greater than fit_range[1] + const_func.SetLineColor(ROOT.kRed) + # const_func.SetLineStyle(ROOT.kDashed) + const_func.Draw("L SAME") fit_func.Print("V") c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.pdf") + c.Close() # Close the canvas after saving # c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.png") # Commented out as plot quality is very bad @@ -211,7 +225,13 @@ def calc_norm_factor(dy_hist, fitting_function, fit_params): h_ratio_points.GetXaxis().SetLabelSize(0.03) line.Draw("SAME") if args.f: + # Plot only until 50 GeV, the value after 50 GeV will be the fit function's value at 50 GeV fit_func.Draw("SAME") + # Draw constant for x > 50 GeV + const_val = fit_func.Eval(fit_range[1]) + const_func = ROOT.TF1("const_func", f"{const_val}", fit_range[1], plot_range[1]) # NOTE - assuming plot_range[1] is greater than fit_range[1] + const_func.SetLineColor(ROOT.kRed) + const_func.Draw("SAME") if fit_result and fit_result.IsValid(): latex = ROOT.TLatex() latex.SetNDC(True) @@ -238,51 +258,54 @@ def calc_norm_factor(dy_hist, fitting_function, fit_params): c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.pdf") # c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.png") -print(f"Integral of DY histogram from 0 to 50 GeV: {integral_histo_DY}") -print(f"Integral of ratio histogram from 0 to 50 GeV: {integral_histo_ratio}") + c_ratio_only.Close() # Close the canvas after saving +print(f"Integral of DY histogram from {fit_range[0]} to {fit_range[1]} GeV: {integral_histo_DY}") +print(f"Integral of ratio histogram from {fit_range[0]} to {fit_range[1]} GeV: {integral_histo_ratio}") # Update dyZpTrw.json -if args.write_json and args.f: +if args.write_json is not None and args.f: wrote = False # 'fit_func', 'fit_result', 'fitfunc' are in scope from the last for-loop # iteration (Python loop variables persist after the loop). - try: - if fit_result and fit_result.IsValid(): - # Build a ROOT / C++ compatible formula string with full precision. - root_formula = fitfunc # e.g. "[0]*TMath::Erf(...) + [3]*x + [4]*x**2 + [5]" - n_params = fit_func.GetNpar() - params = [fit_func.GetParameter(i) for i in range(n_params)] - for i, p in enumerate(params): - root_formula = root_formula.replace(f"[{i}]", f"{p:.6f}") - # Convert Python-style x**2 to ROOT / C++ TMath::Sq(x) - root_formula = root_formula.replace("x**2", "TMath::Sq(x)") - # Tidy up double signs that can appear after parameter substitution - root_formula = root_formula.replace("+ -", "- ") - root_formula = root_formula.replace("- -", "+ ") - # Prepend the integral normalization factor if methodology 2 is chosen - if args.n == 1: - full_expr = f"{norm_factor}*{root_formula}" - - # Read the existing JSON so other years/types are preserved. - existing = {} - if os.path.exists(args.write_json): - try: - with open(args.write_json) as _fj: - existing = json.load(_fj) - except json.JSONDecodeError as _e: - print(f"WARNING: Existing JSON file '{args.write_json}' is malformed " - f"({_e}); it will be overwritten.") - # Update only the requested year / sample-type key. - existing.setdefault(args.year, {})[args.sample_type] = full_expr + if fit_result and fit_result.IsValid(): + # Build a ROOT / C++ compatible formula string with full precision. + root_formula = fitfunc # e.g. "[0]*TMath::Erf(...) + [3]*x + [4]*x**2 + [5]" + n_params = fit_func.GetNpar() + params = [fit_func.GetParameter(i) for i in range(n_params)] + const_val = fit_func.Eval(fit_range[1]) + for i, p in enumerate(params): + root_formula = root_formula.replace(f"[{i}]", f"{p:.6f}") + # Convert Python-style x**2 to ROOT / C++ TMath::Sq(x) + # root_formula = root_formula.replace("x**2", "TMath::Sq(x)") + # Tidy up double signs that can appear after parameter substitution + root_formula = root_formula.replace("+ -", "- ") + root_formula = root_formula.replace("- -", "+ ") + piecewise_formula = f"({root_formula})*(x<{fit_range[1]}) + ({const_val:.6f})*(x>={fit_range[1]})" - with open(args.write_json, "w") as _fj: - json.dump(existing, _fj, indent=4) - _fj.write("\n") - print(f"\nWrote updated dyZpTrw.json → {args.write_json}") - print(f" [{args.year}][{args.sample_type}]: {full_expr}") - wrote = True + # Prepend the integral normalization factor if methodology 2 is chosen + if args.n == 1: + full_expr = f"{norm_factor}*{piecewise_formula}" else: - print("\nWARNING: Fit did not converge; dyZpTrw.json was NOT updated.") - except NameError: - print("\nWARNING: No fit results in scope (was -f passed?). " - "dyZpTrw.json was NOT updated.") \ No newline at end of file + full_expr = piecewise_formula + + # Read the existing JSON so other years/types are preserved. + existing = {} + if os.path.exists(args.write_json): + try: + with open(args.write_json) as _fj: + existing = json.load(_fj) + except json.JSONDecodeError as _e: + print(f"WARNING: Existing JSON file '{args.write_json}' is malformed " + f"({_e}); it will be overwritten.") + # Update only the requested year / sample-type key. + sample_key = f"{args.sample_type}_{args.nj}j" + existing.setdefault(args.year, {})[sample_key] = full_expr + + with open(args.write_json, "w") as _fj: + json.dump(existing, _fj, indent=4) + _fj.write("\n") + print(f"\nWrote updated dyZpTrw.json → {args.write_json}") + print(f" [{args.year}][{sample_key}]: {full_expr}") + wrote = True + else: + print("\nWARNING: Fit did not converge; dyZpTrw.json was NOT updated.") \ No newline at end of file diff --git a/ZpTreweighting/nuisances.py b/ZpTreweighting/2022_v12/nuisances.py similarity index 100% rename from ZpTreweighting/nuisances.py rename to ZpTreweighting/2022_v12/nuisances.py diff --git a/ZpTreweighting/plot.py b/ZpTreweighting/2022_v12/plot.py similarity index 100% rename from ZpTreweighting/plot.py rename to ZpTreweighting/2022_v12/plot.py diff --git a/ZpTreweighting/samples.py b/ZpTreweighting/2022_v12/samples.py similarity index 93% rename from ZpTreweighting/samples.py rename to ZpTreweighting/2022_v12/samples.py index 8d6d9da4..bc8ab026 100644 --- a/ZpTreweighting/samples.py +++ b/ZpTreweighting/2022_v12/samples.py @@ -1,3 +1,7 @@ +# Danush Shekar (UIC) +# Built on top of the DY CR 2022v12 config from this file: https://github.com/latinos/PlotsConfigurationsRun3/blob/2040efccdab8bc42d670a5e2cae8676d7b0f106c/ControlRegions/DY/2022_v12/samples.py + + from mkShapesRDF.lib.search_files import SearchFiles searchFiles = SearchFiles() @@ -15,9 +19,9 @@ dataReco = 'Run2022_ReReco_nAODv12_Full2022v12' dataSteps = 'DATAl2loose2022v12__sblancof__l2loose' elif dataset_samples == 'amassiro': - mcProduction = 'Summer22_130x_nAODv12_Full2022v12' + mcProduction = 'Summer22_130x_nAODv12_Full2022v12_OLD' # new datasets were produced around 11Apr26, using old datasets to compare with prior results (DS, 13Apr26) mcSteps = 'MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight' # Using DYto2L-2Jets_MLL-50 from Amassiro (DS, 21Nov25) - dataReco = 'Run2022_ReReco_nAODv12_Full2022v12' + dataReco = 'Run2022_ReReco_nAODv12_Full2022v12_OLD' # new datasets were produced around 11Apr26, using old datasets to compare with prior results (DS, 13Apr26) dataSteps = 'DATAl2loose2022v12__l2loose' # Choose l2loose sample but apply tight selections in analysis (eleWP and muWP) # fakeSteps = 'DATAl1loose2022EFGv12__fakeW' @@ -142,7 +146,7 @@ def addSampleWeight(samples, sampleName, sampleNameType, weight): 'FilesPerJob': 2, } -addSampleWeight(samples,'DY','DYto2L-2Jets_MLL-50','DY_LO_ZpTrw') +addSampleWeight(samples,'DY','DYto2L-2Jets_MLL-50','DY_NLO_ZpTrw') # remove backgrounds from data for ZpT reweighting: top_samples = ['TTTo2L2Nu', 'TWminusto2L2Nu', 'TbarWplusto2L2Nu']#, 'ST_tW_top'] diff --git a/ZpTreweighting/structure.py b/ZpTreweighting/2022_v12/structure.py similarity index 100% rename from ZpTreweighting/structure.py rename to ZpTreweighting/2022_v12/structure.py diff --git a/ZpTreweighting/variables.py b/ZpTreweighting/2022_v12/variables.py similarity index 99% rename from ZpTreweighting/variables.py rename to ZpTreweighting/2022_v12/variables.py index ec35774b..a00ceb55 100644 --- a/ZpTreweighting/variables.py +++ b/ZpTreweighting/2022_v12/variables.py @@ -24,7 +24,7 @@ variables['ptll'] = { 'name': 'ptll', - 'range' : (160,0,80), + 'range' : (40,0,80), 'xaxis' : 'p_{T}^{ll} [GeV]', 'fold' : 0 } diff --git a/ZpTreweighting/2023_v12/aliases.py b/ZpTreweighting/2023_v12/aliases.py new file mode 100644 index 00000000..cb46c4ed --- /dev/null +++ b/ZpTreweighting/2023_v12/aliases.py @@ -0,0 +1,261 @@ +import os +import copy +import inspect +import json + +configurations = os.path.realpath(inspect.getfile(inspect.currentframe())) # this file + +aliases = {} +aliases = OrderedDict() + +mc = [skey for skey in samples if skey not in ('Fake', 'DATA', 'Dyemb', 'DATA_EG', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] +# Commented out as not used (DS, 19Nov25) +# mc_emb = [skey for skey in samples if skey not in ('Fake', 'DATA', 'DATA_Mu', 'DATA_EMu', 'Fake_EG', 'Fake_Mu', 'Fake_EMu')] + +# Using LepSF2l__ele_cutBased_LooseID_tthMVA_Run3__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 from latest git repo push (https://github.com/latinos/PlotsConfigurationsRun3/blob/f8a0f50dfe6301543203d9d260ad721204a2739f/ControlRegions/DY/2022_v12/aliases.py#L14-L15) +eleWP = 'cutBased_LooseID_tthMVA_Run3' +muWP = 'cut_TightID_pfIsoTight_HWW_tthmva_67' + +aliases['LepWPCut'] = { + 'expr': 'LepCut2l__ele_'+eleWP+'__mu_'+muWP, + 'samples': mc + ['DATA'], +} + +aliases['LepWPSF'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__mu_'+muWP, + 'samples': mc +} + +# gen-matching to prompt only (GenLepMatch2l matches to *any* gen lepton) +aliases['PromptGenLepMatch2l'] = { + 'expr': 'Alt(Lepton_promptgenmatched, 0, 0) * Alt(Lepton_promptgenmatched, 1, 0)', + 'samples': mc +} + +aliases['gen_Zpt'] = { + # 'linesToAdd': [".L /afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+"], + # 'linesToAdd': ['.L /eos/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/HWW_polarization/Extended/getGenZpt.cc+'], + 'linesToAdd': [ + """ +#ifndef getGenZpt +#define getGenZpt + +#include + +#include "TVector2.h" +#include "Math/Vector4Dfwd.h" +#include "Math/GenVector/LorentzVector.h" +#include "Math/GenVector/PtEtaPhiM4D.h" + +#include +#include "ROOT/RVec.hxx" + +using namespace ROOT; +using namespace ROOT::VecOps; + +double GetGenZpt( + int nGenPart, + RVecF GenPart_pt, + RVecI GenPart_pdgId, + RVecI GenPart_genPartIdxMother, + RVecI GenPart_statusFlags, + float gen_ptll + ){ + + + + // Find Gen pT of Z decaying into leptons + unsigned nGen = nGenPart; + std::vector LepCands{}; + std::vector MotherIdx{}; + std::vector MotherPdgId{}; + int pdgId, sFlag, MIdx; + bool hasZ = false; + //std::cout << "==========" << std::endl; + for (unsigned iGen{0}; iGen != nGen; ++iGen){ + pdgId = std::abs(GenPart_pdgId[iGen]); + sFlag = GenPart_statusFlags[iGen]; + //std::cout << pdgId << " ; " << sFlag << " ; " << GenPart_pt->At(iGen) << " ; " << GenPart_genPartIdxMother->At(iGen) << std::endl; + if (((pdgId == 11) || (pdgId == 13) || (pdgId == 15)) && ((sFlag >> 0 & 1) || (sFlag >> 2 & 1) || (sFlag >> 3 & 1) || (sFlag >> 4 & 1))){ + LepCands.push_back(iGen); + MIdx = GenPart_genPartIdxMother[iGen]; + MotherIdx.push_back(MIdx); + if (MIdx > -1){ + MotherPdgId.push_back(GenPart_pdgId[MIdx]); + if (GenPart_pdgId[MIdx]==23) hasZ = true; + }else{ + MotherPdgId.push_back(0); + } + } + } + + //std::cout << "Check:" << std::endl; + for (unsigned iGen{0}; iGen != LepCands.size(); ++iGen){ + for (unsigned jGen{0}; jGen != LepCands.size(); ++jGen){ + if (jGen <= iGen) continue; + //std::cout << iGen << " ; " << MotherIdx[iGen] << " ; " << jGen << " ; " << MotherIdx[jGen] << " ; " << MotherPdgId[iGen] << " ; " << hasZ << std::endl; + // Some DY samples generate the Z; others have the two leptons produced directly -> motherId is 0 for those events + if (hasZ){ + if (MotherIdx[iGen] == MotherIdx[jGen] && MotherPdgId[iGen] == 23) return GenPart_pt[MotherIdx[iGen]]; + }else{ + if (MotherIdx[iGen] == MotherIdx[jGen] && MotherIdx[iGen] == 0) return GenPart_pt[MotherIdx[iGen]]; + } + } + } + //std::cout << "Falling back!" << std::endl; + return gen_ptll; + +} + +#endif + """], + 'class': 'GetGenZpt', + 'args': 'nGenPart, GenPart_pt, GenPart_pdgId, GenPart_genPartIdxMother, GenPart_statusFlags, gen_ptll', + # 'expr': 'gen_ptll', + 'samples': mc +} + +# Jet bins +# using Alt(CleanJet_pt, n, 0) instead of Sum(CleanJet_pt >= 30) because jet pt ordering is not strictly followed in JES-varied samples + +# No jet with pt > 30 GeV +aliases['zeroJet'] = { + 'expr': 'Alt(CleanJet_pt, 0, 0) < 30.' +} + +aliases['oneJet'] = { + 'expr': 'Alt(CleanJet_pt, 0, 0) > 30.' +} + +aliases['multiJet'] = { + 'expr': 'Alt(CleanJet_pt, 1, 0) > 30.' +} + +aliases['noJetInHorn'] = { + 'expr' : 'Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', +} + +# exec(open('dyZpTrw.py', "r").read()) +# aliases['DY_LO_ZpTrw'] = { +# 'expr': '('+DYrew['2022_NLO']['w'].replace('x', 'gen_Zpt')+')*(zeroJet)*(ptll < 50) + 1*(zeroJet)*(ptll >= 50)', +# 'samples': ['DY'] +# } +_dyzptrw_json = os.path.join(os.path.dirname(configurations), 'dyZpTrw.json') +with open(_dyzptrw_json) as _fj: + DYrew = json.load(_fj) +aliases['DY_NLO_ZpTrw'] = { + 'expr': '(' + DYrew['2023']['NLO_0j'].replace('x', 'gen_Zpt') + ')*(zeroJet)' + + ' + (' + DYrew['2023']['NLO_1j'].replace('x', 'gen_Zpt') + ')*(oneJet&& Alt(CleanJet_pt,1,0)<30)' + + ' + (' + DYrew['2023']['NLO_2j'].replace('x', 'gen_Zpt') + ')*(multiJet)', + 'samples': ['DY'] +} + +######################################################################## +# B-Tagging WP: https://btv-wiki.docs.cern.ch/ScaleFactors/Run3Summer22/ +######################################################################## + +# Algo / WP / WP cut +btagging_WPs = { + "DeepFlavB" : {"loose":"0.0479", "medium":"0.2431", "tight":"0.6553", "xtight":"0.7667", "xxtight":"0.9459"}, + "RobustParTAK4B" : {"loose":"0.0681", "medium":"0.3487", "tight":"0.7969", "xtight":"0.8882", "xxtight":"0.9883"}, + "PNetB" : {"loose":"0.0358", "medium":"0.1917", "tight":"0.6172", "xtight":"0.7515", "xxtight":"0.9659"} +} + +# Algo / SF name +btagging_SFs = { + "DeepFlavB" : "deepjet", + "RobustParTAK4B" : "partTransformer", + "PNetB" : "partNet", +} + +# Algorithm and WP selection +bAlgo = 'PNetB' # ['DeepFlavB','RobustParTAK4B','PNetB'] +WP = 'loose' # ['loose','medium','tight','xtight','xxtight'] + +# Access information from dictionaries +bWP = btagging_WPs[bAlgo][WP] +bSF = btagging_SFs[bAlgo] + +# # B tagging selections and scale factors +aliases['bVeto'] = { + 'expr': f'Sum(CleanJet_pt > 20. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) == 0' +} + +aliases['bReq'] = { + 'expr': f'Sum(CleanJet_pt > 30. && abs(CleanJet_eta) < 2.5 && Take(Jet_btag{bAlgo}, CleanJet_jetIdx) > {bWP}) >= 1' +} + +# Commenting out, as this was not included in latest git repo (https://github.com/latinos/PlotsConfigurationsRun3/blob/f8a0f50dfe6301543203d9d260ad721204a2739f/ControlRegions/DY/2022_v12/aliases.py#L89-L104) +# aliases['bVetoSF'] = { +# 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>20 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<20 || abs(CleanJet_eta)>2.5))))'.format(bSF), +# 'samples': mc +# } + +# aliases['bReqSF'] = { +# 'expr': 'TMath::Exp(Sum(LogVec((CleanJet_pt>30 && abs(CleanJet_eta)<2.5)*Take(Jet_btagSF_{}_shape, CleanJet_jetIdx)+1*(CleanJet_pt<30 || abs(CleanJet_eta)>2.5))))'.format(bSF), +# 'samples': mc +# } + +# # Top control region +# aliases['topcr'] = { +# 'expr': 'mtw2>30 && mll>50 && ((zeroJet && !bVeto) || bReq)' +# } + +# # WW control region +# aliases['wwcr'] = { +# 'expr': 'mth>60 && mtw2>30 && mll>100 && bVeto' +# } + +# # Overall b tag SF +# aliases['btagSF'] = { +# 'expr': '(bVeto || (topcr && zeroJet))*bVetoSF + (topcr && !zeroJet)*bReqSF', +# 'samples': mc +# } + +# # Systematic uncertainty variations +# for shift in ['jes','lf','hf','lfstats1','lfstats2','hfstats1','hfstats2','cferr1','cferr2']: + +# for targ in ['bVeto', 'bReq']: +# alias = aliases['%sSF%sup' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) +# alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_up_%s' % shift) + +# alias = aliases['%sSF%sdown' % (targ, shift)] = copy.deepcopy(aliases['%sSF' % targ]) +# alias['expr'] = alias['expr'].replace('btagSF_deepjet_shape', 'btagSF_deepjet_shape_down_%s' % shift) + +# aliases['btagSF%sup' % shift] = { +# 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'up'), +# 'samples': mc +# } + +# aliases['btagSF%sdown' % shift] = { +# 'expr': aliases['btagSF']['expr'].replace('SF', 'SF' + shift + 'down'), +# 'samples': mc +# } + +########################################################################## +# End of b tagging +########################################################################## + +# Data/MC scale factors and systematic uncertainties +aliases['SFweight'] = { + # 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF','btagSF']), + 'expr': ' * '.join(['SFweight2l', 'LepWPCut', 'LepWPSF']), + 'samples': mc +} + +aliases['SFweightEleUp'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__Up', + 'samples': mc +} +aliases['SFweightEleDown'] = { + 'expr': 'LepSF2l__ele_'+eleWP+'__Down', + 'samples': mc +} +aliases['SFweightMuUp'] = { + 'expr': 'LepSF2l__mu_'+muWP+'__Up', + 'samples': mc +} +aliases['SFweightMuDown'] = { + 'expr': 'LepSF2l__mu_'+muWP+'__Down', + 'samples': mc +} diff --git a/ZpTreweighting/2023_v12/automate.py b/ZpTreweighting/2023_v12/automate.py new file mode 100644 index 00000000..76e49d4d --- /dev/null +++ b/ZpTreweighting/2023_v12/automate.py @@ -0,0 +1,744 @@ +#!/usr/bin/env python3 +""" +run_ZpTrw_workflow.py +===================== +Automated Z pT reweighting extraction workflow. + +The script orchestrates the following steps: + + Pre-Phase 1 — prepare configuration files + ------------------------------------------ + 0a. Patch configuration.py: set tag = "{year}_{sample_type}_{original_tag}" + 0b. Comment out the addSampleWeight line for DY pT reweighting in samples.py + (weights cannot be applied before they are derived) + + Round 1 — ZpTreweighting analysis + ---------------------------------- + 1a. mkShapesRDF -c 1 compile configuration + 1b. mkShapesRDF -o 0 -f . -b 1 submit HTCondor jobs + 1c. (wait) poll condor until all jobs finish + 1d. mkShapesRDF -o 2 -f . merge individual ROOT files (hadd) + 2. extract_Zptrw.py extract the Z pT reweighting function + and overwrite dyZpTrw.json + 2b. Move extract_Zptrw.py plots to plots_{year}_{sample_type}_obtainWeights/ + 2c. mkPlot --onlyPlot cratio create comparison plots on the merged file + + Round 2 — main analysis (optional, --second-analysis DIR) + ---------------------------------------------------------- + 3a. Update DYrew key in aliases.py with the correct year/sample-type + 3b. Uncomment addSampleWeight for DY pT reweighting in samples.py + 3c. mkShapesRDF -c 1 compile second analysis + 3d. mkShapesRDF -o 0 -f . -b 1 submit second-round condor jobs + 3e. (wait) poll condor until all jobs finish + 3f. mkShapesRDF -o 2 -f . merge second-round ROOT files + 3g. mkPlot --onlyPlot cratio create comparison plots for second round + +Prerequisites +------------- + * Source mkShapesRDF setup first + +Typical usage +------------- + # Run from inside ZpTreweighting/ or give the folder explicitly: + python automate.py --year 2022 --sample-type LO + + # Also kick off the second-round analysis immediately after: + python automate.py --year 2022 --sample-type LO --second-analysis ./ +""" + +import argparse +import glob +import os +import re +import shutil +import subprocess +import sys +import time + + +def banner(msg): + width = max(60, len(msg) + 4) + print("\n" + "=" * width) + print(f" {msg}") + print("=" * width) + + +def info(msg): + print(f" {msg}") + +# Run commands +def run_cmd(cmd, dry_run=False, cwd=None): + """Print and (optionally) execute *cmd*. + + *cmd* may be a list of strings or a single shell string. + Returns the process exit code (always 0 in dry-run mode). + """ + display = " ".join(cmd) if isinstance(cmd, list) else cmd + info(f"$ {display}") + if dry_run: + return 0 + result = subprocess.run(cmd, cwd=cwd, shell=isinstance(cmd, str)) + return result.returncode + + +def run_cmd_output(cmd, cwd=None): + """Run *cmd* and return *(returncode, stdout, stderr)* as strings.""" + result = subprocess.run( + cmd, + cwd=cwd, + capture_output=True, + text=True, + shell=isinstance(cmd, str), + ) + return result.returncode, result.stdout, result.stderr + + +# HTCondor job tracking +# Return the set of condor cluster IDs found in the job log files. Log files are expected at ``{batch_dir}/{tag}/**/log.txt`` +def _get_cluster_ids_from_logs(batch_dir, tag): + log_pattern = os.path.join(batch_dir, tag, "**", "log.txt") + log_files = glob.glob(log_pattern, recursive=True) + cluster_ids = set() + for log_file in log_files: + try: + with open(log_file) as fh: + for line in fh: + m = re.match(r"000 \((\d+)\.\d+\.\d+\)", line) + if m: + cluster_ids.add(m.group(1)) + except OSError: + pass + return cluster_ids + +# Return the number of jobs still in the condor queue for *cluster_id*. +def _count_condor_jobs_in_cluster(cluster_id): + rc, stdout, _ = run_cmd_output(["condor_q", str(cluster_id)]) + if rc != 0: + # cluster is gone (all jobs completed or removed) + return 0 + m = re.search(r"(\d+) jobs?", stdout) + return int(m.group(1)) if m else 0 + +# Hold until all HTCondor jobs for *tag* are no longer queued. +def wait_for_condor_jobs(batch_dir, tag, poll_interval=120, dry_run=False): + CONDOR_REGISTRATION_DELAY = 10 + + if dry_run: + info("[dry-run] Skipping condor wait.") + return + + # Give HTCondor a few seconds to register newly submitted jobs. + time.sleep(CONDOR_REGISTRATION_DELAY) + + cluster_ids = _get_cluster_ids_from_logs(batch_dir, tag) + if not cluster_ids: + info("WARNING: No condor cluster IDs found in log files. " + "Waiting 60 s and retrying once...") + time.sleep(60) + cluster_ids = _get_cluster_ids_from_logs(batch_dir, tag) + + if not cluster_ids: + info("WARNING: Still no cluster IDs found. " + "Assuming jobs have already completed or were submitted " + "outside HTCondor.") + return + + info(f"Tracking condor cluster(s): {', '.join(sorted(cluster_ids))}") + + while True: + remaining = { + cid for cid in cluster_ids + if _count_condor_jobs_in_cluster(cid) > 0 + } + if not remaining: + info("All condor jobs have completed.") + return + + total = sum(_count_condor_jobs_in_cluster(c) for c in remaining) + info( + f"[{time.strftime('%H:%M:%S')}] " + f"{total} job(s) still queued in " + f"cluster(s) {', '.join(sorted(remaining))}. " + f"Polling again in {poll_interval} s..." + ) + time.sleep(poll_interval) + + +# Configuration reader +# Execute *cfg_file* and return a dict with analysis settings. +def read_configuration(cfg_file): + ns = { + "__file__": os.path.abspath(cfg_file), + "os": os, + "sys": sys, + } + try: + with open(cfg_file) as fh: + exec(compile(fh.read(), cfg_file, "exec"), ns) + except Exception as exc: + # configuration.py may call os.getlogin() which can fail in some + # environments; fall through with whatever was captured so far. + info(f"WARNING: Error while parsing {cfg_file}: {exc}") + + tag = ns.get("tag", "ZpTreweighting") + output_folder = ns.get( + "outputFolder", + os.path.join( + "/eos/user", + os.environ.get("USER", "unknown")[0], + os.environ.get("USER", "unknown"), + "mkShapesRDF_rootfiles", + tag, + "rootFile", + ), + ) + return { + "tag": tag, + "outputFolder": output_folder.rstrip("/"), + "outputFile": ns.get("outputFile", f"mkShapes__{tag}.root"), + "batchFolder": ns.get("batchFolder", "condor"), + } + + +# Workflow phases +# Phase 1a+1b — compile and submit condor jobs. +def phase1_submit(zptrw_dir, dry_run=False): + banner("Phase 1a: Compile ZpTreweighting configuration") + rc = run_cmd(["mkShapesRDF", "-c", "1"], dry_run=dry_run, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: mkShapesRDF -c 1 failed (exit code {rc})") + + banner("Phase 1b: Submit ZpTreweighting condor jobs") + rc = run_cmd( + ["mkShapesRDF", "-o", "0", "-f", ".", "-b", "1"], + dry_run=dry_run, + cwd=zptrw_dir, + ) + if rc != 0: + sys.exit(f"ERROR: mkShapesRDF -o 0 failed (exit code {rc})") + + +# Phase 1c — wait for all condor jobs to finish. +def phase1_wait(zptrw_dir, cfg, poll_interval=120, dry_run=False): + banner("Phase 1c: Waiting for HTCondor jobs to complete") + batch_dir = os.path.join(zptrw_dir, cfg["batchFolder"]) + wait_for_condor_jobs( + batch_dir=batch_dir, + tag=cfg["tag"], + poll_interval=poll_interval, + dry_run=dry_run, + ) + + +# Phase 1d — merge individual job ROOT files (hadd). +def phase1_merge(zptrw_dir, dry_run=False): + banner("Phase 1d: Merge ROOT files") + rc = run_cmd( + ["mkShapesRDF", "-o", "2", "-f", "."], + dry_run=dry_run, + cwd=zptrw_dir, + ) + if rc != 0: + sys.exit(f"ERROR: Phase 1d - mkShapesRDF -o 2 (merge) failed (exit code {rc})") + + +# Configuration / samples file patching helpers + +# Append '{_suffix}' and prepend '{year}_{sample_type}_' to the tag variable in configuration.py. +def patch_configuration_tag(cfg_file, year, sample_type, suffix, dry_run=False): + banner("Patching configuration.py: prepending year/sample-type to tag") + + if not os.path.exists(cfg_file): + info(f"WARNING: {cfg_file} not found; skipping tag patch.") + return + + with open(cfg_file) as fh: + content = fh.read() + + prefix = f"{year}_{sample_type}_" + # Match: tag = "..." or tag = '...' (not already prefixed). + # Build pattern with f-string so the negative lookahead uses the actual prefix. + pattern = re.compile( + r"""^(\s*tag\s*=\s*)["'][^"']*["']""", + re.MULTILINE, + ) + + def _replace(m): + new_tag = f'{year}_{sample_type}_ZpTreweighting_{suffix}' + info(f" tag: → '{new_tag}'") + return f'{m.group(1)}"{new_tag}"' + + new_content, count = pattern.subn(_replace, content, count=1) + if count == 0: + info("WARNING: Could not find 'tag = ...' line in configuration.py; " + "skipping tag patch. Verify that configuration.py contains a " + "tag = \"\" assignment at module level.") + return + + if not dry_run: + with open(cfg_file, "w") as fh: + fh.write(new_content) + info(f" Updated: {cfg_file}") + else: + info("[dry-run] Would update configuration.py tag.") + + +# Comment out the addSampleWeight line for DY pT reweighting in samples.py. +def comment_addsampleweight_dy(samples_file, dry_run=False): + banner("Commenting out addSampleWeight for DY pT reweighting in samples.py") + + if not os.path.exists(samples_file): + info(f"WARNING: {samples_file} not found; skipping.") + return + + with open(samples_file) as fh: + content = fh.read() + + # Match an un-commented addSampleWeight call referencing a DY_*_ZpTrw weight. + # Pattern breakdown: + # ^(?![ \t]*#) — line must not start with optional whitespace + '#' + # ([ \t]*addSampleWeight — capture indentation + function name + # \s*\([^)]* — opening paren and any args + # ['"]DY_..._ZpTrw['"] — the DY ZpTrw weight argument + # [^)]*\)) — remaining args + closing paren + pattern = re.compile( + r"^(?![ \t]*#)([ \t]*addSampleWeight\s*\([^)]*['\"]DY_[A-Za-z0-9]+_ZpTrw['\"][^)]*\))", + re.MULTILINE, + ) + + if not pattern.search(content): + info("WARNING: No uncommented addSampleWeight DY ZpTrw line found; skipping.") + return + + new_content = pattern.sub(r"# \1", content) + info(" Commented out addSampleWeight DY ZpTrw line.") + + if not dry_run: + with open(samples_file, "w") as fh: + fh.write(new_content) + info(f" Updated: {samples_file}") + else: + info("[dry-run] Would comment out addSampleWeight DY ZpTrw line.") + +# Uncomment the addSampleWeight line for DY pT reweighting in samples.py. +def uncomment_addsampleweight_dy(samples_file, dry_run=False): + banner("Uncommenting addSampleWeight for DY pT reweighting in samples.py") + + if not os.path.exists(samples_file): + info(f"WARNING: {samples_file} not found; skipping.") + return + + with open(samples_file) as fh: + content = fh.read() + + # Match a commented addSampleWeight call referencing a DY_*_ZpTrw weight. + pattern = re.compile( + r"^([ \t]*)#[ \t]*(addSampleWeight\s*\([^)]*['\"]DY_[A-Za-z0-9]+_ZpTrw['\"][^)]*\))", + re.MULTILINE, + ) + + if not pattern.search(content): + info("WARNING: No commented addSampleWeight DY ZpTrw line found; skipping.") + return + + new_content = pattern.sub(r"\1\2", content) + info(" Uncommented addSampleWeight DY ZpTrw line.") + + if not dry_run: + with open(samples_file, "w") as fh: + fh.write(new_content) + info(f" Updated: {samples_file}") + else: + info("[dry-run] Would uncomment addSampleWeight DY ZpTrw line.") + +# Replace DYrew['old_year']['old_type'] with DYrew['{year}']['{sample_type}'] in aliases.py. +def update_aliases_dyrew_key(aliases_file, year, sample_type, dry_run=False): + banner(f"Updating DYrew key in {os.path.basename(aliases_file)}") + + if not os.path.exists(aliases_file): + info(f"WARNING: {aliases_file} not found; skipping DYrew key update.") + return + + with open(aliases_file) as fh: + content = fh.read() + + # Match DYrew['oldyear']['oldtype_jetbin'] or DYrew["oldyear"]["oldtype_jetbin"] + pattern = re.compile( + r"""DYrew\[\s*(['"])[^'"]+\1\s*\]\[\s*(['"])[^'"]+_(0j|1j|2j)\2\s*\]""" + ) + + def replacer(match): + quote1, quote2, jetbin = match.group(1), match.group(2), match.group(3) + return f"DYrew[{quote1}{year}{quote1}][{quote2}{sample_type}_{jetbin}{quote2}]" + + matches = pattern.findall(content) + if not matches: + info(f"No DYrew['...']['...'] references found in {aliases_file}; skipping.") + return + + new_content = pattern.sub(replacer, content) + info(f" Updated {len(matches)} DYrew key reference(s) → ['{year}']['{sample_type}_']") + + if not dry_run: + with open(aliases_file, "w") as fh: + fh.write(new_content) + info(f" Updated: {aliases_file}") + else: + info(f"[dry-run] Would update DYrew key in {aliases_file}.") + +# Plot helpers +# Move plots produced by extract_Zptrw.py into plots_{year}_{sample_type}_obtainWeights/. +def move_zptrw_plots(zptrw_dir, year, sample_type, folder_suffix="", dry_run=False): + banner("Moving extract_Zptrw.py plots to archive folder") + + target_dir = os.path.join(zptrw_dir, f"extractPlots_{year}_{sample_type}_{folder_suffix}") + plot_files = ( + glob.glob(os.path.join(zptrw_dir, "ZpTreweighting_*.pdf")) + + glob.glob(os.path.join(zptrw_dir, "ZpTreweighting_*.png")) + ) + + if not plot_files: + info("No ZpTreweighting_*.pdf/png files found to move.") + return + + info(f"Target folder: {target_dir}") + for src in plot_files: + dest = os.path.join(target_dir, os.path.basename(src)) + info(f" {os.path.basename(src)} → {os.path.relpath(dest, zptrw_dir)}") + if not dry_run: + os.makedirs(target_dir, exist_ok=True) + shutil.move(src, dest) + + if dry_run: + info("[dry-run] Would create target folder and move plot files.") + +# Run ``mkPlot --onlyPlot cratio --showIntegralLegend 1 --fileFormats png``. +def run_mkplot(analysis_dir, dry_run=False): + banner(f"Running mkPlot in {analysis_dir}") + rc = run_cmd( + ["mkPlot", "--onlyPlot", "cratio", "--showIntegralLegend", "1", + "--fileFormats", "png"], + dry_run=dry_run, + cwd=analysis_dir, + ) + if rc != 0: + info(f"WARNING: mkPlot exited with code {rc}. Continuing workflow.") + +# Phase 2 — run extract_Zptrw.py to derive weights and update dyZpTrw.json, or make the ratio plots after applying the weights. +def phase2_extract(zptrw_dir, cfg, year="2022", sample_type="LO", run_fit = "", dry_run=False): + banner("Phase 2: Extract Z pT reweighting function → update dyZpTrw.json") + + merged_file = os.path.join(cfg["outputFolder"], cfg["outputFile"]) + dyzptrw_json = os.path.join(zptrw_dir, "dyZpTrw.json") + extract_script = os.path.join(zptrw_dir, "extract_Zptrw.py") + + if not dry_run and not os.path.exists(merged_file): + sys.exit( + f"ERROR: Merged ROOT file not found:\n" + f" {merged_file}\n" + f"Run 'mkShapesRDF -o 2 -f .' in {zptrw_dir} first." + ) + for njet in [0, 1, 2]: + # derive weights in Z->MuMu channel, 0 jet bin + if run_fit == "-f": + # define normalization when fitting is requested, else, just plot + cmd = [sys.executable, extract_script, "-f", "-n", "2", "-c", "mm", "-nj", str(njet), "--input", merged_file, "--write-json", dyzptrw_json, "--year", year, "--sample-type", sample_type] + else: + cmd = [sys.executable, extract_script, "-c", "mm", "-nj", str(njet), "--input", merged_file, "--write-json", dyzptrw_json, "--year", year, "--sample-type", sample_type] + rc = run_cmd(cmd, dry_run=dry_run, cwd=zptrw_dir) + # Make plots in Z->ee channel, 0 jet bin + cmd = [sys.executable, extract_script, "-c", "ee", "-nj", str(njet), "--input", merged_file] + rc = run_cmd(cmd, dry_run=dry_run, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: extract_Zptrw.py failed (exit code {rc})") + info(f"dyZpTrw.json updated for nJet={njet} → {dyzptrw_json}") + +# Phase 3 — prepare, compile, submit, wait, merge, and plot second-round analysis. +def phase3_second_round(second_dir, year, sample_type, poll_interval=120, + skip_second_wait=False, skip_second_merge=False, + dry_run=False): + banner(f"Phase 3: Second-round mkShapesRDF in\n {second_dir}") + + # Update aliases.py and samples.py before compiling + aliases_file = os.path.join(second_dir, "aliases.py") + update_aliases_dyrew_key(aliases_file, year, sample_type, dry_run=dry_run) + + samples_file = os.path.join(second_dir, "samples.py") + uncomment_addsampleweight_dy(samples_file, dry_run=dry_run) + + rc = run_cmd(["mkShapesRDF", "-c", "1"], dry_run=dry_run, cwd=second_dir) + if rc != 0: + sys.exit( + f"ERROR: mkShapesRDF -c 1 failed in {second_dir} (exit code {rc})" + ) + + rc = run_cmd( + ["mkShapesRDF", "-o", "0", "-f", ".", "-b", "1"], + dry_run=dry_run, + cwd=second_dir, + ) + if rc != 0: + sys.exit( + f"ERROR: mkShapesRDF -o 0 failed in {second_dir} (exit code {rc})" + ) + + # Wait for second-round condor jobs + second_cfg_file = os.path.join(second_dir, "configuration.py") + has_cfg = os.path.exists(second_cfg_file) + + if not skip_second_wait: + if has_cfg or dry_run: + banner("Phase 3c: Waiting for second-round HTCondor jobs to complete") + second_cfg = ( + read_configuration(second_cfg_file) + if has_cfg + else {"tag": "unknown", "batchFolder": "condor", + "outputFolder": ".", "outputFile": "output.root"} + ) + batch_dir = os.path.join(second_dir, second_cfg["batchFolder"]) + wait_for_condor_jobs( + batch_dir=batch_dir, + tag=second_cfg["tag"], + poll_interval=poll_interval, + dry_run=dry_run, + ) + else: + info(f"WARNING: No configuration.py found in {second_dir}; " + "skipping second-round condor wait.") + else: + info("\n[skip-second-wait] Skipping second-round condor wait.") + + # Merge second-round ROOT files + if not skip_second_merge: + banner("Phase 3d: Merge second-round ROOT files") + rc = run_cmd( + ["mkShapesRDF", "-o", "2", "-f", "."], + dry_run=dry_run, + cwd=second_dir, + ) + if rc != 0: + sys.exit( + f"ERROR: mkShapesRDF -o 2 (merge) failed in {second_dir} " + f"(exit code {rc})" + ) + else: + info("\n[skip-second-merge] Skipping second-round ROOT file merge.") + + # Create RDF plots for the second-round merged output + run_mkplot(second_dir, dry_run=dry_run) + + +# CLI +def parse_args(): + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + # ---- paths ---- + parser.add_argument( + "--zptrw-folder", + default=".", + metavar="DIR", + help="Path to the ZpTreweighting analysis folder " + "(default: current directory).", + ) + parser.add_argument( + "--second-analysis", + default=None, + metavar="DIR", + help="Path to the second-round analysis folder. " + "When given, Phase 3 updates aliases/samples, compiles, submits, " + "waits, merges, and runs mkPlot there after dyZpTrw.py has been updated.", + ) + + # ---- physics ---- + parser.add_argument( + "--year", + default="2022", + help="Year key written to DYrew in dyZpTrw.json (default: '2022').", + ) + parser.add_argument( + "--sample-type", + default="LO", + help="Sample-type key written to DYrew in dyZpTrw.json " + "(default: 'LO').", + ) + + # ---- condor polling ---- + parser.add_argument( + "--poll-interval", + type=int, + default=120, + metavar="SECONDS", + help="Seconds between condor_q polls while waiting for jobs " + "(default: 120).", + ) + + # ---- skip flags ---- + skip = parser.add_argument_group("skip flags (for re-running partial workflow)") + skip.add_argument( + "--skip-submit", + action="store_true", + help="Skip Phase 1a+1b (assume jobs are already running or done).", + ) + skip.add_argument( + "--skip-wait", + action="store_true", + help="Skip Phase 1c (assume all jobs have already finished).", + ) + skip.add_argument( + "--skip-merge", + action="store_true", + help="Skip Phase 1d (assume the merged ROOT file already exists).", + ) + skip.add_argument( + "--skip-extract", + action="store_true", + help="Skip Phase 2 (assume dyZpTrw.json is already up to date).", + ) + skip.add_argument( + "--skip-second-wait", + action="store_true", + help="Skip waiting for second-round HTCondor jobs (Phase 3c).", + ) + skip.add_argument( + "--skip-second-merge", + action="store_true", + help="Skip merging second-round ROOT files (Phase 3d).", + ) + + # ---- misc ---- + parser.add_argument( + "--dry-run", + action="store_true", + help="Print every command that would be run without executing it.", + ) + + return parser.parse_args() + + +def main(): + args = parse_args() + + zptrw_dir = os.path.abspath(args.zptrw_folder) + if not os.path.isdir(zptrw_dir): + sys.exit(f"ERROR: ZpTreweighting folder not found: {zptrw_dir}") + + cfg_file = os.path.join(zptrw_dir, "configuration.py") + if not os.path.exists(cfg_file): + sys.exit(f"ERROR: configuration.py not found in {zptrw_dir}") + + banner("Z pT reweighting workflow") + info(f"ZpTreweighting folder : {zptrw_dir}") + if args.dry_run: + info("*** DRY-RUN mode — no commands will be executed ***") + + # ---- Pre-Phase 1: Patch configuration.py tag and comment out DY pT rw weight ---- + patch_configuration_tag(cfg_file, args.year, args.sample_type, suffix="obtainWeights", dry_run=args.dry_run) + + cfg = read_configuration(cfg_file) + info(f"tag : {cfg['tag']}") + info(f"outputFolder : {cfg['outputFolder']}") + info(f"outputFile : {cfg['outputFile']}") + info(f"batchFolder : {cfg['batchFolder']}") + + samples_file = os.path.join(zptrw_dir, "samples.py") + comment_addsampleweight_dy(samples_file, dry_run=args.dry_run) + + # ---- Phase 1a+1b: Submit ---- + if not args.skip_submit: + phase1_submit(zptrw_dir, dry_run=args.dry_run) + else: + info("\n[skip-submit] Skipping job submission.") + + # ---- Phase 1c: Wait ---- + if not args.skip_wait and not args.skip_submit: + phase1_wait( + zptrw_dir, + cfg, + poll_interval=args.poll_interval, + dry_run=args.dry_run, + ) + elif args.skip_wait: + info("\n[skip-wait] Skipping condor wait.") + + # ---- Phase 1d: Merge ---- + if not args.skip_merge: + phase1_merge(zptrw_dir, dry_run=args.dry_run) + else: + info("\n[skip-merge] Skipping ROOT file merge.") + + # ---- Phase 2: Extract + Update ---- + if not args.skip_extract: + phase2_extract(zptrw_dir, cfg, year=args.year, sample_type=args.sample_type, run_fit="-f", dry_run=args.dry_run) + # Move plots produced by extract_Zptrw.py to archive folder + move_zptrw_plots(zptrw_dir, args.year, args.sample_type, folder_suffix = "obtainWeights", dry_run=args.dry_run) + # Create RDF plots from the merged ROOT file + run_mkplot(zptrw_dir, dry_run=args.dry_run) + # Rename condor and config folders: + cmd = ["mv", os.path.join(zptrw_dir, "condor/"), os.path.join(zptrw_dir, f"condor_{args.year}_{args.sample_type}_obtainWeights")] + rc = run_cmd(cmd, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: failed to rename condor directory (exit code {rc})") + cmd = ["pwd"] + rc = run_cmd(cmd, cwd=zptrw_dir) + cmd = ["mv", os.path.join(zptrw_dir, "configs/"), os.path.join(zptrw_dir, f"configs_{args.year}_{args.sample_type}_obtainWeights")] + rc = run_cmd(cmd, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: failed to rename configs directory (exit code {rc})") + + else: + info("\n[skip-extract] Skipping weight extraction, plot archiving, and mkPlot " + "(assumes dyZpTrw.json and plots are already up to date).") + + # ---- Phase 3 (optional): Second analysis ---- + if args.second_analysis: + second_dir = os.path.abspath(args.second_analysis) + if not os.path.isdir(second_dir): + sys.exit(f"ERROR: Second-analysis folder not found: {second_dir}") + + patch_configuration_tag(cfg_file, args.year, args.sample_type, suffix="afterReweighting", dry_run=args.dry_run) + cfg = read_configuration(cfg_file) + info(f"tag : {cfg['tag']}") + info(f"outputFolder : {cfg['outputFolder']}") + info(f"outputFile : {cfg['outputFile']}") + info(f"batchFolder : {cfg['batchFolder']}") + + phase3_second_round( + second_dir, + year=args.year, + sample_type=args.sample_type, + poll_interval=args.poll_interval, + skip_second_wait=args.skip_second_wait, + skip_second_merge=args.skip_second_merge, + dry_run=args.dry_run, + ) + phase2_extract(zptrw_dir, cfg, year=args.year, sample_type=args.sample_type, run_fit="", dry_run=args.dry_run) + # Move plots produced by extract_Zptrw.py to folder + move_zptrw_plots(zptrw_dir, args.year, args.sample_type, folder_suffix = "afterReweighting", dry_run=args.dry_run) + # Create comparison plots from the merged ROOT file + run_mkplot(zptrw_dir, dry_run=args.dry_run) + # Rename log, condor, and config folders: + cmd = ["mv", os.path.join(zptrw_dir, "condor/"), os.path.join(zptrw_dir, f"condor_{args.year}_{args.sample_type}_afterReweighting")] + rc = run_cmd(cmd, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: failed to rename condor directory (exit code {rc})") + cmd = ["mv", os.path.join(zptrw_dir, "configs/"), os.path.join(zptrw_dir, f"configs_{args.year}_{args.sample_type}_afterReweighting")] + rc = run_cmd(cmd, cwd=zptrw_dir) + if rc != 0: + sys.exit(f"ERROR: failed to rename configs directory (exit code {rc})") + + banner("Workflow complete!") + if args.second_analysis: + info("Second-round analysis finished. Plots are in the analysis folder.") + else: + info("dyZpTrw.json has been updated.") + info("To run the second-round analysis:") + info(" cd ") + info(" mkShapesRDF -c 1") + info(" mkShapesRDF -o 0 -f . -b 1") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ZpTreweighting/2023_v12/configuration.py b/ZpTreweighting/2023_v12/configuration.py new file mode 100644 index 00000000..4beaa2de --- /dev/null +++ b/ZpTreweighting/2023_v12/configuration.py @@ -0,0 +1,90 @@ +import sys,os + +# tag used to identify the configuration folder version +tag = "ZpTreweighting" + +# file to use as runner script, default uses mkShapesRDF.shapeAnalysis.runner, otherwise specify path to script +runnerFile = "default" + +# output file name +outputFile = "mkShapes__{}.root".format(tag) + +# path to ouput folder +outputFolder = "/eos/user/" + os.getlogin()[0] + "/" + os.getlogin() + "/mkShapesRDF_rootfiles/" + tag + "/rootFile/" + +# path to batch folder (used for condor submission) +batchFolder = "condor" + +# path to configuration folder (will contain all the compiled configuration files) +configsFolder = "configs" + +# luminosity to normalize to (in 1/fb) +lumi = 17.794 + +# file with dict of aliases to define +aliasesFile = "aliases.py" + +# file with dict of variables +variablesFile = "variables.py" + +# file with dict of cuts +cutsFile = "cuts.py" + +# file with dict of samples +samplesFile = "samples.py" + +# file with dict of samples +plotFile = "plot.py" + +# file with dict of structure (used to define combine processes) +structureFile = "structure.py" + +# nuisances file for mkDatacards and for mkShape +nuisancesFile = "nuisances.py" + +# path to folder where to save plots +plotPath = "plots_" + tag + +# this lines are executed right before the runner on the condor node +mountEOS = [ + # "export KRB5CCNAME=/home/gpizzati/krb5\n", +] + +# list of imports to import when compiling the whole configuration folder, it should not contain imports used by configuration.py +imports = ["os", "glob", ("collections", "OrderedDict"), "ROOT"] + +# list of files to compile +filesToExec = [ + samplesFile, + aliasesFile, + variablesFile, + cutsFile, + plotFile, + nuisancesFile, + structureFile, +] + +# list of variables to keep in the compiled configuration folder +varsToKeep = [ + "batchVars", + "outputFolder", + "batchFolder", + "configsFolder", + "outputFile", + "runnerFile", + "tag", + "samples", + "aliases", + "variables", + ("cuts", {"cuts": "cuts", "preselections": "preselections"}), + ("plot", {"plot": "plot", "groupPlot": "groupPlot", "legend": "legend"}), + "nuisances", + "structure", + "lumi", +] + +# list of variables to keep in the batch submission script (script.py) +batchVars = varsToKeep[varsToKeep.index("samples") :] + + +varsToKeep += ['plotPath'] diff --git a/ZpTreweighting/2023_v12/cuts.py b/ZpTreweighting/2023_v12/cuts.py new file mode 100644 index 00000000..41a5ca00 --- /dev/null +++ b/ZpTreweighting/2023_v12/cuts.py @@ -0,0 +1,59 @@ +cuts = {} + +# Preselections - applied to all the cuts, noJetInHorn replaced by zeroJet +preselections = 'Lepton_pt[0] > 25 \ + && Lepton_pt[1] > 13 \ + && (nLepton >= 2 && Alt(Lepton_pt,2,0) < 10) \ + && abs(Lepton_eta[0]) < 2.5 \ + && abs(Lepton_eta[1]) < 2.5 \ + && mll > 60 \ + && mll < 120 \ +' +# Remove zeroJet preselection as weights are being derived in nJet bins (DS, 18Mar26) + # && zeroJet \ + +# Individual cuts and categories + +# Commenting out the inclusive cuts (DS, 19Nov25) +# cuts['Zee_incl'] = '(Lepton_pdgId[0] * Lepton_pdgId[1] == -11*11)' +# cuts['Zmm_incl'] = '(Lepton_pdgId[0] * Lepton_pdgId[1] == -13*13)' + +cuts['Zee'] = { + 'expr' : '(Lepton_pdgId[0] * Lepton_pdgId[1] == -11*11)', + 'categories' : { + '0j' : 'zeroJet', + '1j' : 'oneJet && Alt(CleanJet_pt,1,0)<30', + '2j' : 'multiJet', + } +} + +cuts['Zmm'] = { + 'expr' : '(Lepton_pdgId[0] * Lepton_pdgId[1] == -13*13)', + 'categories' : { + '0j' : 'zeroJet', + '1j' : 'oneJet && Alt(CleanJet_pt,1,0)<30', + '2j' : 'multiJet', + } +} + +# cuts['Zee_noJetInHorn_incl'] = '(Lepton_pdgId[0] * Lepton_pdgId[1] == -11*11) && Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0' + +# cuts['Zmm_noJetInHorn_incl'] = '(Lepton_pdgId[0] * Lepton_pdgId[1] == -13*13) && Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0' + +# cuts['Zee_noJetInHorn'] = { +# 'expr' : '(Lepton_pdgId[0] * Lepton_pdgId[1] == -11*11) && Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', +# 'categories' : { +# '0j' : 'zeroJet', +# '1j' : 'oneJet && Alt(CleanJet_pt,1,0)<30', +# '2j' : 'multiJet', +# } +# } + +# cuts['Zmm_noJetInHorn'] = { +# 'expr' : '(Lepton_pdgId[0] * Lepton_pdgId[1] == -13*13) && Sum(CleanJet_pt > 30 && CleanJet_pt < 50 && abs(CleanJet_eta) > 2.6 && abs(CleanJet_eta) < 3.1) == 0', +# 'categories' : { +# '0j' : 'zeroJet', +# '1j' : 'oneJet && Alt(CleanJet_pt,1,0)<30', +# '2j' : 'multiJet', +# } +# } diff --git a/ZpTreweighting/2023_v12/dyZpTrw.json b/ZpTreweighting/2023_v12/dyZpTrw.json new file mode 100644 index 00000000..3a247a9b --- /dev/null +++ b/ZpTreweighting/2023_v12/dyZpTrw.json @@ -0,0 +1,17 @@ +{ + "2022": { + "NLO_0j": "0.843196*(0.211634*TMath::Erf((x-9.120552)/3.728196)-0.024628*x+0.000400*TMath::Sq(x)+1.361984)", + "NLO_1j": "0.843196*(0.211634*TMath::Erf((x-9.120552)/3.728196)-0.024628*x+0.000400*TMath::Sq(x)+1.361984)", + "NLO_2j": "0.843196*(0.211634*TMath::Erf((x-9.120552)/3.728196)-0.024628*x+0.000400*TMath::Sq(x)+1.361984)" + }, + "2023": { + "NLO_0j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)", + "NLO_1j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)", + "NLO_2j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)" + }, + "2024": { + "NLO_0j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)", + "NLO_1j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)", + "NLO_2j": "1.0*(1.0*TMath::Erf((x-1.0)/1.0)-1.0*x+1.0*TMath::Sq(x)+1.0)" + } +} \ No newline at end of file diff --git a/ZpTreweighting/2023_v12/dyZpTrw.py b/ZpTreweighting/2023_v12/dyZpTrw.py new file mode 100644 index 00000000..ebeea418 --- /dev/null +++ b/ZpTreweighting/2023_v12/dyZpTrw.py @@ -0,0 +1,6 @@ +{ + "2022_NLO": { + "w2": "1.077825*(0.167840*TMath::Erf((x-9.120662)/3.728448)-0.019531*x+0.000317*TMath::Sq(x)+1.080110)" + "w": "1.0133492539254507*0.843196*(0.211634*TMath::Erf((x-9.120552)/3.728196)-0.024628*x+0.000400*TMath::Sq(x)+1.361984)", + } +} \ No newline at end of file diff --git a/ZpTreweighting/2023_v12/extract_Zptrw.py b/ZpTreweighting/2023_v12/extract_Zptrw.py new file mode 100644 index 00000000..ef8bff4c --- /dev/null +++ b/ZpTreweighting/2023_v12/extract_Zptrw.py @@ -0,0 +1,311 @@ +# ================================= +# Danush Shekar (UIC), 9Dec25 +# ================================= +import json +import os +import ROOT +import mplhep as hep +import matplotlib.pyplot as plt +import numpy as np +from ROOT import TFitResultPtr +import argparse +# CMS plot style +hep.style.use("CMS") +style = hep.style.CMS +style["font.size"] = 18 +plt.style.use(style) + +parser = argparse.ArgumentParser(description='Extract data and fit with Gaussian.') +parser.add_argument('-f', action='store_true', help='Fit the ratio plot using Erf.') +parser.add_argument('-n', type=int, default=0, help='Normalization method:\n1: Ratio of integral of MC over weights*MC.\n2: Normalize MC to data integral before calculating rw factor.') +parser.add_argument('-c', type=str, default="mm", help='Z decay channel.') +parser.add_argument('-nj', type=int, default=0, help='Jet bin category(number of jet bins).') +parser.add_argument('-i', '--input', default='mkShapes__ZpTreweighting.root', help='Path to the merged ROOT file (default: mkShapes__ZpTreweighting.root)') +parser.add_argument('--write-json', default=None, help='If given, write the updated dyZpTrw.json to this path after a successful fit (requires -f). The file is overwritten.') +parser.add_argument('--year', default='2022', help="Year key in the DYrew dict written to dyZpTrw.json (default: '2022')") +parser.add_argument('--sample-type', default='LO', help="Sample-type key in the DYrew dict written to dyZpTrw.json (default: 'LO')") +parser.add_argument('--plot-xrange', type=float, default=80, help='maximum X-axis range for the plots (default: 80)') +parser.add_argument('--fit-xrange', type=float, default=50, help='maximum X-axis range for the fits (default: 50)') +args = parser.parse_args() + +root_file = ROOT.TFile(args.input) +channel = f"Z{args.c}_{args.nj}j" +zee_dir = root_file.Get(channel) +ptll_dir = zee_dir.Get("ptll") + +histo_DY = ptll_dir.Get("histo_DY") +histo_DATA = ptll_dir.Get("histo_DATA") +histo_top = ptll_dir.Get("histo_top") +histo_diboson = ptll_dir.Get("histo_diboson") +histo_SMhiggs = ptll_dir.Get("histo_SMhiggs") + +# Subtract DY backgrounds from DATA +histo_trueData = histo_DATA.Clone("histo_trueData") # Create a clone for the result +histo_trueData.Add(histo_top, -1) +histo_trueData.Add(histo_diboson, -1) +histo_trueData.Add(histo_SMhiggs, -1) + +plot_range = [0, args.plot_xrange] +fit_range = [0, args.fit_xrange] + +def calc_norm_factor(dy_hist, fitting_function, fit_params): + numerator = 0.0 + denominator = 0.0 + first_bin = dy_hist.FindBin(fit_range[0]) + last_bin = dy_hist.FindBin(fit_range[1]) + print("\nNOTE: Using fit function (", fitting_function,") to calculate normalization factor.\n") # [0]*TMath::Erf((x-[1])/[2]) + [3]*x + [4]*x**2 + [5] + for bin_idx in range(first_bin, last_bin + 1): + mc_events = dy_hist.GetBinContent(bin_idx) + # weight = histo_ratio.GetBinContent(bin_idx) + binCenter = dy_hist.GetXaxis().GetBinCenter(bin_idx) + weight = fit_params[0]*ROOT.TMath.Erf((binCenter - fit_params[1])/fit_params[2]) + fit_params[3]*binCenter + fit_params[4]*binCenter**2 + fit_params[5] + numerator += mc_events + denominator += mc_events * weight + norm_factor = numerator / denominator if denominator != 0 else 1.0 + print("Normalization factor:", norm_factor) + return norm_factor + +# # Rebin hists +# histo_trueData.Rebin(4) +# histo_DY.Rebin(4) + +# Calculate the integral/sum of histo_DY and histo_ratio for x axis in [0, fit_range[1]) +integral_histo_DY = histo_DY.Integral(histo_DY.FindBin(fit_range[0]), histo_DY.FindBin(fit_range[1]) - 1) +integral_histo_DATA = histo_trueData.Integral(histo_trueData.FindBin(fit_range[0]), histo_trueData.FindBin(fit_range[1]) - 1) +norm_factor2 = integral_histo_DATA/integral_histo_DY +print("Normalization factor 2:", norm_factor2) +if args.n == 2: + histo_DY.Scale(norm_factor2) + integral_histo_DYscaled = histo_DY.Integral(histo_DY.FindBin(fit_range[0]), histo_DY.FindBin(fit_range[1]) - 1) + +# Create a ratio plot of DATA to DY +histo_ratio = histo_trueData.Clone("histo_ratio") +histo_ratio.Divide(histo_DY) + +integral_histo_ratio = histo_ratio.Integral(histo_ratio.FindBin(fit_range[0]), histo_ratio.FindBin(fit_range[1]) - 1) + +# fitting_functions = ["[0]*x**6 + [1]*x**5 + [2]*x**4 + [3]*x**3 + [4]*x**2 + [5]*x + [6]", "[0]*([1]*TMath::Erf((x-[2])/[3]) + [4]*x + [5]*x**2)"] +fitting_functions = ["([0]*TMath::Erf((x-[1])/[2]) + [3]*x + [4]*TMath::Sq(x) + [5])"] +initial_guesses = [[0.0, 5.0, 10.0, 0.0, 0.0, 1.0]] +save_name_suffixes = [channel] +for fitfunc, initguess, savename in zip(fitting_functions, initial_guesses, save_name_suffixes): + c = ROOT.TCanvas("c", "c", 1000, 1000) + c.Divide(1,2) + + # Top pad: main plot + pad1 = c.cd(1) + pad1.SetPad(0.0, 0.30, 1.0, 1.0) + pad1.SetBottomMargin(0.02) + pad1.SetLogy() # if you want log-y + + histo_DY.SetTitle("") + histo_DY.GetYaxis().SetTitle("Events / 5 GeV") + histo_DY.GetXaxis().SetLabelSize(0) + histo_DY.GetYaxis().SetTitleSize(0.06) + histo_DY.GetYaxis().SetTitleOffset(0.8) + histo_DY.GetYaxis().SetLabelSize(0.05) + histo_DY.GetXaxis().SetRangeUser(plot_range[0], plot_range[1]) + # Draw DY as reference, then data points + # histo_DY.SetLineColor(ROOT.kBlue) + histo_DY.SetStats(False) + histo_DY.SetFillStyle(3344) + histo_DY.SetFillColorAlpha(ROOT.kBlue, 0.1) + histo_DY.Draw("HIST") + histo_trueData.SetMarkerStyle(8) + histo_trueData.SetMarkerColor(ROOT.kBlack) + histo_trueData.SetMarkerSize(0.8) + histo_trueData.GetXaxis().SetRangeUser(plot_range[0], plot_range[1]) + histo_trueData.Draw("E1 SAME") + + # CMS label + label = ROOT.TLatex() + label.SetNDC(True) + label.SetTextSize(0.040) + label.DrawLatex(0.12, 0.92, "#bf{CMS} #it{Preliminary}") + label.DrawLatex(0.55, 0.92, "L = 8.2 fb^{-1} (#sqrt{s} = 13.6 TeV)") + label.DrawLatex(0.15, 0.2, f"num(DY) events in ({fit_range[0]},{fit_range[1]}) GeV = {integral_histo_DY:.3f}") + label.DrawLatex(0.15, 0.15, f"num(DATA) events in ({fit_range[0]},{fit_range[1]}) GeV = {integral_histo_DATA:.3f}") + if args.n == 2: + label.DrawLatex(0.15, 0.1, f"num(DY normalized) events in ({fit_range[0],fit_range[1]}) GeV = {integral_histo_DYscaled:.3f}") + + + leg = ROOT.TLegend(0.60, 0.70, 0.88, 0.88) + leg.SetBorderSize(0) + leg.SetFillStyle(0) + leg.AddEntry(histo_trueData, "Data - BG", "pe") + leg.AddEntry(histo_DY, "DY", "f") + leg.Draw() + + # Bottom pad: ratio + pad2 = c.cd(2) + pad2.SetPad(0.0, 0.0, 1.0, 0.30) + pad2.SetTopMargin(0.02) + pad2.SetBottomMargin(0.35) + + h_ratio_points = histo_ratio.Clone("h_ratio_points") + h_ratio_points.SetTitle("") + h_ratio_points.SetMarkerStyle(20) + h_ratio_points.SetMarkerSize(0.8) + h_ratio_points.SetLineColor(ROOT.kBlack) + h_ratio_points.SetMarkerColor(ROOT.kBlack) + + h_ratio_points.GetYaxis().SetTitle("(Data-BG)/DY") + h_ratio_points.GetYaxis().SetNdivisions(505) + h_ratio_points.GetYaxis().SetTitleSize(0.12) + h_ratio_points.GetYaxis().SetTitleOffset(0.30) + h_ratio_points.GetYaxis().SetLabelSize(0.08) + h_ratio_points.GetXaxis().SetTitle("p_{T}^{ll} [GeV]") + h_ratio_points.GetXaxis().SetTitleSize(0.12) + h_ratio_points.GetXaxis().SetLabelSize(0.10) + h_ratio_points.GetXaxis().SetRangeUser(plot_range[0], plot_range[1]) + + h_ratio_points.SetMinimum(0.0) + h_ratio_points.SetMaximum(2.0) + + h_ratio_points.Draw("E1") + h_ratio_points.SetStats(False) + + # horizontal line at 1 + line = ROOT.TLine(h_ratio_points.GetXaxis().GetXmin(), 1.0, + h_ratio_points.GetXaxis().GetXmax(), 1.0) + line.SetLineColor(ROOT.kGray+2) + line.SetLineStyle(2) + line.Draw("SAME") + if args.f: + # --- Fit the ratio with an error function --- + # erf(x; p0, p1, p2, p3) = p0 + p1 * TMath::Erf((x - p2)/p3) + fit_func = ROOT.TF1("fit_erf", + fitfunc, + fit_range[0],#histo_ratio.GetXaxis().GetXmin(), + fit_range[1])#histo_ratio.GetXaxis().GetXmax()) + fit_func.SetParameters(*initguess) # reasonable starting values + fit_func.SetLineColor(ROOT.kRed) + + fit_result = h_ratio_points.Fit(fit_func, "RVS") # fit only in the visible x-range + if fit_result and fit_result.IsValid(): + chi2 = fit_result.Chi2() + ndf = fit_result.Ndf() + chi2_ndf = chi2 / ndf if ndf != 0 else float('inf') + print(f" ============ Chi2/NDF: {chi2_ndf:.3f} ============") + latex = ROOT.TLatex() + latex.SetNDC(True) + latex.SetTextSize(0.08) + latex.SetTextColor(ROOT.kBlack) + latex.DrawLatex(0.15, 0.55, f"#chi^{{2}}/ndf = {chi2_ndf:.2f}") + func_formula = fit_func.GetTitle() # or fit_func.GetExpFormula() for TF1 + param_values = [fit_func.GetParameter(i) for i in range(fit_func.GetNpar())] + param_str = ", ".join([f"p{i}={v:.2f}" for i, v in enumerate(param_values)]) + # # Display fit function and parameters + # latex.DrawLatex(0.15, 0.65, f"f(x) = {func_formula}") + # latex.DrawLatex(0.15, 0.75, param_str) + else: + print("Fit failed or invalid - cannot compute Chi2/NDF") + + # Plot only until 50 GeV, the value after 50 GeV will be the fit function's value at 50 GeV + fit_func.Draw("SAME") + const_val = fit_func.Eval(fit_range[1]) + const_func = ROOT.TF1("const_func", f"{const_val}", fit_range[1], plot_range[1]) # NOTE - assuming plot_range[1] is greater than fit_range[1] + const_func.SetLineColor(ROOT.kRed) + # const_func.SetLineStyle(ROOT.kDashed) + const_func.Draw("L SAME") + fit_func.Print("V") + c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.pdf") + c.Close() # Close the canvas after saving + # c.SaveAs(f"ZpTreweighting_with_ratio_{savename}.png") + + # Commented out as plot quality is very bad + c_ratio_only = ROOT.TCanvas("c_ratio_only", "c_ratio_only", 800, 800) + h_ratio_points.Draw("E1") + h_ratio_points.GetYaxis().SetTitle("(Data-BG)/DY") + h_ratio_points.GetYaxis().SetTitleSize(0.05) + h_ratio_points.GetYaxis().SetTitleOffset(0.8) + h_ratio_points.GetYaxis().SetLabelSize(0.04) + h_ratio_points.GetXaxis().SetTitle("p_{T}^{ll} [GeV]") + h_ratio_points.GetXaxis().SetTitleSize(0.05) + h_ratio_points.GetXaxis().SetLabelSize(0.03) + line.Draw("SAME") + if args.f: + # Plot only until 50 GeV, the value after 50 GeV will be the fit function's value at 50 GeV + fit_func.Draw("SAME") + # Draw constant for x > 50 GeV + const_val = fit_func.Eval(fit_range[1]) + const_func = ROOT.TF1("const_func", f"{const_val}", fit_range[1], plot_range[1]) # NOTE - assuming plot_range[1] is greater than fit_range[1] + const_func.SetLineColor(ROOT.kRed) + const_func.Draw("SAME") + if fit_result and fit_result.IsValid(): + latex = ROOT.TLatex() + latex.SetNDC(True) + latex.SetTextSize(0.025) + latex.SetTextColor(ROOT.kBlack) + latex.DrawLatex(0.15, 0.15, f"#chi^{{2}}/ndf = {chi2_ndf:.2f}") + func_formula = fit_func.GetTitle() # or fit_func.GetExpFormula() for TF1 + param_values = [fit_func.GetParameter(i) for i in range(fit_func.GetNpar())] + param_str = ", ".join([f"p{i}={v:.2f}" for i, v in enumerate(param_values)]) + # Display fit function and parameters + latex.DrawLatex(0.15, 0.35, f"f(x) = {func_formula}") + latex.DrawLatex(0.15, 0.3, param_str) + if args.n == 1: + norm_factor = calc_norm_factor(histo_DY, fitfunc, param_values) + latex.DrawLatex(0.15, 0.25, f"Normalization factor = {norm_factor:.2f}") + print(f"Normalization factor = {norm_factor}") + formula = fit_func.GetTitle() # e.g., "[0]*x + [1]" + n_params = fit_func.GetNpar() + params = [fit_func.GetParameter(i) for i in range(n_params)] + # Replace [i] with parameter values + for i, p in enumerate(params): + formula = formula.replace(f"[{i}]", f"{p:.3f}") + print(f"Fit function with parameters: {formula}") + + c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.pdf") + # c_ratio_only.SaveAs(f"ZpTreweighting_ratio_fit_{savename}.png") + c_ratio_only.Close() # Close the canvas after saving +print(f"Integral of DY histogram from {fit_range[0]} to {fit_range[1]} GeV: {integral_histo_DY}") +print(f"Integral of ratio histogram from {fit_range[0]} to {fit_range[1]} GeV: {integral_histo_ratio}") + +# Update dyZpTrw.json +if args.write_json is not None and args.f: + wrote = False + # 'fit_func', 'fit_result', 'fitfunc' are in scope from the last for-loop + # iteration (Python loop variables persist after the loop). + if fit_result and fit_result.IsValid(): + # Build a ROOT / C++ compatible formula string with full precision. + root_formula = fitfunc # e.g. "[0]*TMath::Erf(...) + [3]*x + [4]*x**2 + [5]" + n_params = fit_func.GetNpar() + params = [fit_func.GetParameter(i) for i in range(n_params)] + const_val = fit_func.Eval(fit_range[1]) + for i, p in enumerate(params): + root_formula = root_formula.replace(f"[{i}]", f"{p:.6f}") + # Convert Python-style x**2 to ROOT / C++ TMath::Sq(x) + # root_formula = root_formula.replace("x**2", "TMath::Sq(x)") + # Tidy up double signs that can appear after parameter substitution + root_formula = root_formula.replace("+ -", "- ") + root_formula = root_formula.replace("- -", "+ ") + piecewise_formula = f"({root_formula})*(x<{fit_range[1]}) + ({const_val:.6f})*(x>={fit_range[1]})" + + # Prepend the integral normalization factor if methodology 2 is chosen + if args.n == 1: + full_expr = f"{norm_factor}*{piecewise_formula}" + else: + full_expr = piecewise_formula + + # Read the existing JSON so other years/types are preserved. + existing = {} + if os.path.exists(args.write_json): + try: + with open(args.write_json) as _fj: + existing = json.load(_fj) + except json.JSONDecodeError as _e: + print(f"WARNING: Existing JSON file '{args.write_json}' is malformed " + f"({_e}); it will be overwritten.") + # Update only the requested year / sample-type key. + sample_key = f"{args.sample_type}_{args.nj}j" + existing.setdefault(args.year, {})[sample_key] = full_expr + + with open(args.write_json, "w") as _fj: + json.dump(existing, _fj, indent=4) + _fj.write("\n") + print(f"\nWrote updated dyZpTrw.json → {args.write_json}") + print(f" [{args.year}][{sample_key}]: {full_expr}") + wrote = True + else: + print("\nWARNING: Fit did not converge; dyZpTrw.json was NOT updated.") \ No newline at end of file diff --git a/ZpTreweighting/2023_v12/nuisances.py b/ZpTreweighting/2023_v12/nuisances.py new file mode 100644 index 00000000..bd87d720 --- /dev/null +++ b/ZpTreweighting/2023_v12/nuisances.py @@ -0,0 +1,52 @@ +print(treeBaseDir) +def makeMCDirectory(var=''): + _treeBaseDir = treeBaseDir + '' + if useXROOTD: + _treeBaseDir = redirector + treeBaseDir + if var== '': + return '/'.join([_treeBaseDir, mcProduction, mcSteps]) + else: + return '/'.join([_treeBaseDir, mcProduction, mcSteps + '__' + var]) + + +# merge cuts +_mergedCuts = [] +for cut in list(cuts.keys()): + __cutExpr = '' + if type(cuts[cut]) == dict: + __cutExpr = cuts[cut]['expr'] + for cat in list(cuts[cut]['categories'].keys()): + _mergedCuts.append(cut + '_' + cat) + elif type(cuts[cut]) == str: + _mergedCuts.append(cut) + + +# Dfinitions of groups of samples +mc = [skey for skey in samples if skey not in ('DATA')] + +nuisances = {} + + +################################ EXPERIMENTAL UNCERTAINTIES ################################# + +#### Luminosity + +# https://twiki.cern.ch/twiki/bin/view/CMS/LumiRecommendationsRun3 +nuisances['lumi_2023'] = { + 'name' : 'lumi_2023', + 'type' : 'lnN', + 'samples' : dict((skey, '1.013') for skey in mc) +} + +### MC statistical uncertainty +autoStats = True +if autoStats: + ## Use the following if you want to apply the automatic combine MC stat nuisances. + nuisances['stat'] = { + 'type': 'auto', + 'maxPoiss': '10', + 'includeSignal': '0', + # nuisance ['maxPoiss'] = Number of threshold events for Poisson modelling + # nuisance ['includeSignal'] = Include MC stat nuisances on signal processes (1=True, 0=False) + 'samples': {} + } diff --git a/ZpTreweighting/2023_v12/plot.py b/ZpTreweighting/2023_v12/plot.py new file mode 100644 index 00000000..c2bef728 --- /dev/null +++ b/ZpTreweighting/2023_v12/plot.py @@ -0,0 +1,67 @@ +# Group plot +# Groups of samples to improve the plots. +# If not defined, normal plots is used + +groupPlot = {} + +groupPlot['DY'] = { + 'nameHR' : 'DY', + 'isSignal' : 0, + 'color' : 420, # kGreen+4 + 'samples' : ['DY'] +} + +groupPlot['background'] = { + 'nameHR' : 'background', + 'isSignal' : 0, + 'color' : 851, # kAzure -9 + 'samples' : ['top', 'diboson', 'SMhiggs'] +} + + +# Plot +# keys here must match keys in samples.py + +plot = {} + +plot['DY'] = { + 'color' : 420, # kGreen+4 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + +plot['top'] = { + 'color' : 400, # kYellow + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + +plot['diboson'] = { + 'color' : 851, # kAzure -9 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + +plot['SMhiggs'] = { + 'color' : 632+3, # kRed+3 + 'isSignal' : 0, + 'isData' : 0, + 'scale' : 1.0, +} + +# Data +plot['DATA'] = { + 'nameHR' : 'Data', + 'color' : 1 , + 'isSignal' : 0, + 'isData' : 1 , + 'isBlind' : 0 +} + +# Legend definition +legend = {} +legend['lumi'] = 'L = 17.8 fb^{-1}' +legend['sqrt'] = '#sqrt{s} = 13.6 TeV' diff --git a/ZpTreweighting/2023_v12/samples.py b/ZpTreweighting/2023_v12/samples.py new file mode 100644 index 00000000..ccbdfaae --- /dev/null +++ b/ZpTreweighting/2023_v12/samples.py @@ -0,0 +1,205 @@ +from mkShapesRDF.lib.search_files import SearchFiles + +searchFiles = SearchFiles() +redirector = "" + +useXROOTD = False + +# MC: /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight +# DATA: /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight +mcProduction = 'Summer23_130x_nAODv12_Full2023v12_OLD' # new datasets were produced around 11Apr26, using old datasets to compare with prior results (DS, 13Apr26) +mcSteps = 'MCl2loose2023v12__MCCorr2023v12JetScaling__l2tight' # Using DYto2L-2Jets_MLL-50 from Amassiro (DS, 21Nov25) +dataReco = 'Run2023_Prompt_nAODv12_Full2023v12_OLD' # new datasets were produced around 11Apr26, using old datasets to compare with prior results (DS, 13Apr26) +dataSteps = 'DATAl2loose2023v12__l2loose' # Choose l2loose sample but apply tight selections in analysis (eleWP and muWP) + +# fakeSteps = 'DATAl1loose2022EFGv12__fakeW' + +############################################## +###### Tree base directory for the site ###### +############################################## +treeBaseDir = f'/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano' +limitFiles = -1 # For running on smaller set of samples (DS, 21Nov25) + +def makeMCDirectory(var=""): + _treeBaseDir = treeBaseDir + "" + if redirector != "": + _treeBaseDir = redirector + treeBaseDir + if var == "": + return "/".join([_treeBaseDir, mcProduction, mcSteps]) + else: + return "/".join([_treeBaseDir, mcProduction, mcSteps + "__" + var]) + + +mcDirectory = makeMCDirectory() +# fakeDirectory = os.path.join(treeBaseDir, dataReco, fakeSteps) +dataDirectory = os.path.join(treeBaseDir, dataReco, dataSteps) + +samples = {} + + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + return [(name, _files[:limitFiles])] + else: + return [(name, _files)] + + +def CombineBaseW(samples, proc, samplelist): + _filtFiles = list(filter(lambda k: k[0] in samplelist, samples[proc]["name"])) + _files = list(map(lambda k: k[1], _filtFiles)) + _l = list(map(lambda k: len(k), _files)) + leastFiles = _files[_l.index(min(_l))] + dfSmall = ROOT.RDataFrame("Runs", leastFiles) + s = dfSmall.Sum("genEventSumw").GetValue() + f = ROOT.TFile(leastFiles[0]) + t = f.Get("Events") + t.GetEntry(1) + xs = t.baseW * s + + __files = [] + for f in _files: + __files += f + df = ROOT.RDataFrame("Runs", __files) + s = df.Sum("genEventSumw").GetValue() + newbaseW = str(xs / s) + weight = newbaseW + "/baseW" + + for iSample in samplelist: + addSampleWeight(samples, proc, iSample, weight) + + +def addSampleWeight(samples, sampleName, sampleNameType, weight): + obj = list(filter(lambda k: k[0] == sampleNameType, samples[sampleName]["name"]))[0] + samples[sampleName]["name"] = list( + filter(lambda k: k[0] != sampleNameType, samples[sampleName]["name"]) + ) + if len(obj) > 2: + samples[sampleName]["name"].append( + (obj[0], obj[1], obj[2] + "*(" + weight + ")") + ) + else: + samples[sampleName]["name"].append((obj[0], obj[1], "(" + weight + ")")) + + +################################################ +############ DATA DECLARATION ################## +################################################ + +# Putting for later: HLT selections (DS, 19Nov25) +DataRun = [ + ['Cv1','Run2023C-Prompt-v1'], + ['Cv2','Run2023C-Prompt-v2'], + ['Cv3','Run2023C-Prompt-v3'], + ['Cv4','Run2023C-Prompt-v4'], +] + + +DataSets = ['MuonEG','Muon0','Muon1','EGamma0','EGamma1'] + +DataTrig = { + 'MuonEG' : 'Trigger_ElMu' , + 'Muon0' : '!Trigger_ElMu && (Trigger_sngMu || Trigger_dblMu)', + 'Muon1' : '!Trigger_ElMu && (Trigger_sngMu || Trigger_dblMu)', + 'EGamma0' : '!Trigger_ElMu && !Trigger_sngMu && !Trigger_dblMu && (Trigger_sngEl || Trigger_dblEl)', + 'EGamma1' : '!Trigger_ElMu && !Trigger_sngMu && !Trigger_dblMu && (Trigger_sngEl || Trigger_dblEl)' +} + +######################################### +############ MC COMMON ################## +######################################### + +# SFweight does not include btag weights +mcCommonWeightNoMatch = 'XSWeight*METFilter_Common*SFweight' +mcCommonWeight = 'XSWeight*METFilter_Common*PromptGenLepMatch2l*SFweight' + +#mcCommonWeight = 'XSWeight*METFilter_Common*SFweight' + +########################################### +############# BACKGROUNDS ############### +########################################### + +# DY +files = nanoGetSampleFiles(mcDirectory, 'DYto2L-2Jets_MLL-50') + + +samples['DY'] = { + 'name': files, + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + +addSampleWeight(samples,'DY','DYto2L-2Jets_MLL-50','DY_NLO_ZpTrw') + +# remove backgrounds from data for ZpT reweighting: +top_samples = ['TTTo2L2Nu', 'TWminusto2L2Nu', 'TbarWplusto2L2Nu']#, 'ST_tW_top'] +diboson_samples = ['WWTo2L2Nu', 'WZTo3LNu', 'GluGlutoContintoWWtoENuENu', 'GluGlutoContintoWWtoENuMuNu', 'GluGlutoContintoWWtoENuTauNu', 'GluGlutoContintoWWtoMuNuENu', 'GluGlutoContintoWWtoMuNuMuNu', 'GluGlutoContintoWWtoMuNuTauNu', 'GluGlutoContintoWWtoTauNuENu', 'GluGlutoContintoWWtoTauNuMuNu', 'GluGlutoContintoWWtoTauNuTauNu', 'WGtoLNuG-1J_PTG10to100', 'WGtoLNuG-1J_PTG100to200', 'WGtoLNuG-1J_PTG200to400', 'WGtoLNuG-1J_PTG400to600', 'WGtoLNuG-1J_PTG600'] +higgs_samples = ['GluGluHToWWTo2L2Nu_M125', 'VBFHToWWTo2L2Nu_M125'] + +samples['top'] = { + 'name': + nanoGetSampleFiles(mcDirectory, 'ST_t-channel_top') + \ + nanoGetSampleFiles(mcDirectory, 'ST_t-channel_antitop') + \ + nanoGetSampleFiles(mcDirectory, 'ST_s-channel_plus') + \ + nanoGetSampleFiles(mcDirectory, 'ST_s-channel_minus') + \ + nanoGetSampleFiles(mcDirectory, 'ST_tW_top') + \ + nanoGetSampleFiles(mcDirectory, 'ST_tW_antitop') + \ + nanoGetSampleFiles(mcDirectory, 'TTTo2L2Nu') + \ + nanoGetSampleFiles(mcDirectory, 'TWminusto2L2Nu') + \ + nanoGetSampleFiles(mcDirectory, 'TbarWplusto2L2Nu'), + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + +samples['diboson'] = { + 'name': nanoGetSampleFiles(mcDirectory, 'WWTo2L2Nu') + \ + nanoGetSampleFiles(mcDirectory, 'WZTo3LNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoENuENu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoENuMuNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoENuTauNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoMuNuENu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoMuNuMuNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoMuNuTauNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoTauNuENu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoTauNuMuNu') + \ + nanoGetSampleFiles(mcDirectory, 'GluGlutoContintoWWtoTauNuTauNu') + \ + nanoGetSampleFiles(mcDirectory, 'WGtoLNuG-1J_PTG10to100') + \ + nanoGetSampleFiles(mcDirectory, 'WGtoLNuG-1J_PTG100to200') + \ + nanoGetSampleFiles(mcDirectory, 'WGtoLNuG-1J_PTG200to400') + \ + nanoGetSampleFiles(mcDirectory, 'WGtoLNuG-1J_PTG400to600') + \ + nanoGetSampleFiles(mcDirectory, 'WGtoLNuG-1J_PTG600'), + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + +samples['SMhiggs'] = { + 'name': nanoGetSampleFiles(mcDirectory, 'GluGluHToWWTo2L2Nu_M125') + \ + nanoGetSampleFiles(mcDirectory, 'VBFHToWWTo2L2Nu_M125'), + 'weight': mcCommonWeight, + 'FilesPerJob': 2, +} + + +########################################### +################## DATA ################### +########################################### + +samples['DATA'] = { + 'name': [], + 'weight': 'LepWPCut*METFilter_DATA', + 'weights': [], + 'isData': ['all'], + 'FilesPerJob': 15 +} + +for _, sd in DataRun: + for pd in DataSets: + datatag = pd + '_' + sd + + files = nanoGetSampleFiles(dataDirectory, datatag) + + print(datatag) + + samples['DATA']['name'].extend(files) + addSampleWeight(samples, 'DATA', datatag, DataTrig[pd]) + diff --git a/ZpTreweighting/2023_v12/structure.py b/ZpTreweighting/2023_v12/structure.py new file mode 100644 index 00000000..4c0f5428 --- /dev/null +++ b/ZpTreweighting/2023_v12/structure.py @@ -0,0 +1,30 @@ +# structure configuration for datacard + +structure = {} + +# keys here must match keys in samples.py +structure['DY'] = { + 'isSignal' : 0, + 'isData' : 0 +} + +structure['top'] = { + 'isSignal' : 0, + 'isData' : 0 +} + +structure['diboson'] = { + 'isSignal' : 0, + 'isData' : 0 +} + +structure['SMhiggs'] = { + 'isSignal' : 0, + 'isData' : 0 +} + +# data +structure['DATA'] = { + 'isSignal' : 0, + 'isData' : 1 +} diff --git a/ZpTreweighting/2023_v12/variables.py b/ZpTreweighting/2023_v12/variables.py new file mode 100644 index 00000000..a00ceb55 --- /dev/null +++ b/ZpTreweighting/2023_v12/variables.py @@ -0,0 +1,176 @@ +# variables +variables = {} + +variables['events'] = { + 'name' : '1', + 'range' : (1,0,2), + 'xaxis' : 'events', + 'fold' : 3 +} + +variables['nvtx'] = { + 'name' : 'PV_npvsGood', + 'range' : (100, 0, 100), + 'xaxis' : 'number of vertices', + 'fold' : 3 +} + +variables['mll'] = { + 'name': 'mll', + 'range' : (50,50,150), + 'xaxis' : 'm_{ll} [GeV]', + 'fold' : 0 +} + +variables['ptll'] = { + 'name': 'ptll', + 'range' : (40,0,80), + 'xaxis' : 'p_{T}^{ll} [GeV]', + 'fold' : 0 +} + +# Data samples do not have gen-level information +# variables['gen_ptll'] = { +# 'name': 'gen_ptll', +# 'range' : (40,0,200), +# 'xaxis' : 'gen p_{T}^{ll} [GeV]', +# 'fold' : 0 +# } + +# variables['gen_Zpt'] = { +# 'name': 'gen_Zpt(nGenPart, GenPart_pt, GenPart_pdgId, GenPart_genPartIdxMother, GenPart_statusFlags, gen_ptll)', +# 'range' : (40,0,200), +# 'xaxis' : 'Gen p_{T}^{Z} [GeV]', +# 'fold' : 0 +# } + +variables['drll'] = { + 'name': 'drll', + 'range' : (50, 0,5), + 'xaxis' : '#Delta R_{ll}', + 'fold' : 0 +} + +variables['dphill'] = { + 'name': 'dphill', + 'range' : (50,0,5), + 'xaxis' : '#Delta #phi_{ll}', + 'fold' : 0 +} + +variables['pt1'] = { + 'name': 'Lepton_pt[0]', + 'range' : (40,0,200), + 'xaxis' : 'p_{T} 1st lep', + 'fold' : 3 +} + +variables['pt2'] = { + 'name': 'Lepton_pt[1]', + 'range' : (40,0,160), + 'xaxis' : 'p_{T} 2nd lep', + 'fold' : 3 +} + +variables['eta1'] = { + 'name': 'Lepton_eta[0]', + 'range' : (50,-2.5,2.5), + 'xaxis' : '#eta 1st lep', + 'fold' : 3 +} + +variables['eta2'] = { + 'name': 'Lepton_eta[1]', + 'range' : (50,-2.5,2.5), + 'xaxis' : '#eta 2nd lep', + 'fold' : 3 +} + + +# B Tag +variables['jetdeepb'] = { + 'name': 'Alt(Take(Jet_btagDeepFlavB, CleanJet_jetIdx), 0, -99)', + 'range' : (40,-1,1), + 'xaxis' : 'B tagger 1st jet (DeepB)', + 'fold' : 2 +} + +variables['jetParT'] = { + 'name': 'Alt(Take(Jet_btagRobustParTAK4B, CleanJet_jetIdx), 0, -99)', + 'range' : (40,-1,1), + 'xaxis' : 'B tagger 1st jet (RobustParT AK4B)', + 'fold' : 2 +} + +variables['jetPNetB'] = { + 'name': 'Alt(Take(Jet_btagPNetB, CleanJet_jetIdx), 0, -99)', + 'range' : (40,-1,1), + 'xaxis' : 'B tagger 1st jet (ParticleNet B)', + 'fold' : 2 +} + + +# MET +variables['trkMet'] = { + 'name': 'TkMET_pt', + 'range' : (20,0,200), + 'xaxis' : 'trk met [GeV]', + 'fold' : 3 +} + +variables['puppimet'] = { + 'name': 'PuppiMET_pt', + 'range' : (20,0,200), + 'xaxis' : 'Puppi MET p_{T} [GeV]', + 'fold' : 3 +} + +############# New Jet processing +variables['njet'] = { + 'name': 'Sum(CleanJet_pt>30)', + 'range' : (5,0,5), + 'xaxis' : 'Number of jets', + 'fold' : 2 +} + +variables['jetpt1'] = { + 'name': 'Alt(CleanJet_pt, 0, -99) - 9999.9*(CleanJet_pt[0]<30)', + 'range' : (40,0,200), + 'xaxis' : 'p_{T} 1st jet', + 'fold' : 0 +} + +variables['jetpt2'] = { + 'name': 'Alt(CleanJet_pt, 1, -99) - 9999.9*(CleanJet_pt[1]<30)', + 'range' : (40,0,200), + 'xaxis' : 'p_{T} 2nd jet', + 'fold' : 0 +} + +variables['jeteta1'] = { + 'name': 'Alt(CleanJet_eta, 0, -99) - 9999.9*(CleanJet_pt[0]<30)', + 'range' : (30,-4.7,4.7), + 'xaxis' : '#eta 1st jet', + 'fold' : 0 +} + +variables['jeteta1_fine_binning'] = { + 'name': 'Alt(CleanJet_eta, 0, -99) - 9999.9*(CleanJet_pt[0]<30)', + 'range' : (94,-4.7,4.7), + 'xaxis' : '#eta 1st jet', + 'fold' : 0 +} + +variables['jeteta2'] = { + 'name': 'Alt(CleanJet_eta, 1, -99) - 9999.9*(CleanJet_pt[1]<30)', + 'range' : (30,-4.7,4.7), + 'xaxis' : '#eta 2nd jet', + 'fold' : 0 +} + +variables['jeteta2_fine_binning'] = { + 'name': 'Alt(CleanJet_eta, 1, -99) - 9999.9*(CleanJet_pt[1]<30)', + 'range' : (94,-4.7,4.7), + 'xaxis' : '#eta 2nd jet', + 'fold' : 0 +} diff --git a/ZpTreweighting/automate.py b/ZpTreweighting/automate.py deleted file mode 100644 index d028574d..00000000 --- a/ZpTreweighting/automate.py +++ /dev/null @@ -1,505 +0,0 @@ -#!/usr/bin/env python3 -""" -run_ZpTrw_workflow.py -===================== -Automated Z pT reweighting extraction workflow. - -The script orchestrates the following steps: - - Round 1 — ZpTreweighting analysis - ---------------------------------- - 1a. mkShapesRDF -c 1 compile configuration - 1b. mkShapesRDF -o 0 -f . -b 1 submit HTCondor jobs - 1c. (wait) poll condor until all jobs finish - 1d. mkShapesRDF -o 2 -f . merge individual ROOT files (hadd) - 2. extract_Zptrw.py extract the Z pT reweighting function - and overwrite dyZpTrw.json - - Round 2 — main analysis (optional) - ----------------------------------- - 3a. mkShapesRDF -c 1 compile second analysis - 3b. mkShapesRDF -o 0 -f . -b 1 submit second-round condor jobs - -Prerequisites -------------- - * mkShapesRDF must be on PATH (source the mkShapesRDF start.sh first). - * The merged ROOT output lives on EOS; EOS must be mounted (lxplus default). - * Python packages required by extract_Zptrw.py must be available (ROOT, - mplhep, matplotlib, numpy). - -Typical usage -------------- - # Run from inside ZpTreweighting/ or give the folder explicitly: - python run_ZpTrw_workflow.py - - # Also kick off the second-round analysis immediately after: - python run_ZpTrw_workflow.py --second-analysis ../HWW/ggH_SF/2022 - - # Skip submission if jobs were already submitted: - python run_ZpTrw_workflow.py --skip-submit - - # Dry run to see what commands would be executed: - python run_ZpTrw_workflow.py --dry-run -""" - -import argparse -import glob -import os -import re -import subprocess -import sys -import time - - -# --------------------------------------------------------------------------- -# Pretty printing helpers -# --------------------------------------------------------------------------- - -def banner(msg): - width = max(60, len(msg) + 4) - print("\n" + "=" * width) - print(f" {msg}") - print("=" * width) - - -def info(msg): - print(f" {msg}") - - -# --------------------------------------------------------------------------- -# Command execution -# --------------------------------------------------------------------------- - -def run_cmd(cmd, dry_run=False, cwd=None): - """Print and (optionally) execute *cmd*. - - *cmd* may be a list of strings or a single shell string. - Returns the process exit code (always 0 in dry-run mode). - """ - display = " ".join(cmd) if isinstance(cmd, list) else cmd - info(f"$ {display}") - if dry_run: - return 0 - result = subprocess.run(cmd, cwd=cwd, shell=isinstance(cmd, str)) - return result.returncode - - -def run_cmd_output(cmd, cwd=None): - """Run *cmd* and return *(returncode, stdout, stderr)* as strings.""" - result = subprocess.run( - cmd, - cwd=cwd, - capture_output=True, - text=True, - shell=isinstance(cmd, str), - ) - return result.returncode, result.stdout, result.stderr - - -# --------------------------------------------------------------------------- -# HTCondor job tracking -# --------------------------------------------------------------------------- - -def _get_cluster_ids_from_logs(batch_dir, tag): - """Return the set of condor cluster IDs found in the job log files. - - Log files are expected at ``{batch_dir}/{tag}/**/log.txt`` (the standard - layout produced by mkShapesRDF). The cluster ID is extracted from lines - matching the condor event-log format:: - - 000 (CLUSTERID.PROCID.SUBPROCID) DATE TIME Job submitted from host... - """ - log_pattern = os.path.join(batch_dir, tag, "**", "log.txt") - log_files = glob.glob(log_pattern, recursive=True) - cluster_ids = set() - for log_file in log_files: - try: - with open(log_file) as fh: - for line in fh: - m = re.match(r"000 \((\d+)\.\d+\.\d+\)", line) - if m: - cluster_ids.add(m.group(1)) - except OSError: - pass - return cluster_ids - - -def _count_condor_jobs_in_cluster(cluster_id): - """Return the number of jobs still in the condor queue for *cluster_id*. - - Runs ``condor_q `` and parses the summary line, e.g.:: - - 3 jobs; 0 completed; 0 removed; 3 idle; 0 running; 0 held; 0 suspended - """ - rc, stdout, _ = run_cmd_output(["condor_q", str(cluster_id)]) - if rc != 0: - # cluster is gone (all jobs completed or removed) - return 0 - m = re.search(r"(\d+) jobs?", stdout) - return int(m.group(1)) if m else 0 - - -def wait_for_condor_jobs(batch_dir, tag, poll_interval=120, dry_run=False): - """Block until all HTCondor jobs for *tag* are no longer queued. - - Parameters - ---------- - batch_dir: - Directory that contains the ``{tag}/`` sub-tree of condor job folders. - tag: - Analysis tag (matches the sub-directory name inside ``batch_dir``). - poll_interval: - Seconds to wait between condor_q polls. - dry_run: - If True, return immediately without checking anything. - """ - # Seconds to wait after submission before the first condor_q poll, so - # HTCondor has time to register the newly submitted jobs. - CONDOR_REGISTRATION_DELAY = 10 - - if dry_run: - info("[dry-run] Skipping condor wait.") - return - - # Give HTCondor a few seconds to register newly submitted jobs. - time.sleep(CONDOR_REGISTRATION_DELAY) - - cluster_ids = _get_cluster_ids_from_logs(batch_dir, tag) - if not cluster_ids: - info("WARNING: No condor cluster IDs found in log files. " - "Waiting 60 s and retrying once...") - time.sleep(60) - cluster_ids = _get_cluster_ids_from_logs(batch_dir, tag) - - if not cluster_ids: - info("WARNING: Still no cluster IDs found. " - "Assuming jobs have already completed or were submitted " - "outside HTCondor.") - return - - info(f"Tracking condor cluster(s): {', '.join(sorted(cluster_ids))}") - - while True: - remaining = { - cid for cid in cluster_ids - if _count_condor_jobs_in_cluster(cid) > 0 - } - if not remaining: - info("All condor jobs have completed.") - return - - total = sum(_count_condor_jobs_in_cluster(c) for c in remaining) - info( - f"[{time.strftime('%H:%M:%S')}] " - f"{total} job(s) still queued in " - f"cluster(s) {', '.join(sorted(remaining))}. " - f"Polling again in {poll_interval} s..." - ) - time.sleep(poll_interval) - - -# --------------------------------------------------------------------------- -# Configuration reader -# --------------------------------------------------------------------------- - -def read_configuration(cfg_file): - """Execute *cfg_file* and return a dict with analysis settings. - - Reads ``tag``, ``outputFolder``, ``outputFile``, and ``batchFolder`` - from ``configuration.py``. - """ - ns = { - "__file__": os.path.abspath(cfg_file), - "os": os, - "sys": sys, - } - try: - with open(cfg_file) as fh: - exec(compile(fh.read(), cfg_file, "exec"), ns) - except Exception as exc: - # configuration.py may call os.getlogin() which can fail in some - # environments; fall through with whatever was captured so far. - info(f"WARNING: Error while parsing {cfg_file}: {exc}") - - tag = ns.get("tag", "ZpTreweighting") - output_folder = ns.get( - "outputFolder", - os.path.join( - "/eos/user", - os.environ.get("USER", "unknown")[0], - os.environ.get("USER", "unknown"), - "mkShapesRDF_rootfiles", - tag, - "rootFile", - ), - ) - return { - "tag": tag, - "outputFolder": output_folder.rstrip("/"), - "outputFile": ns.get("outputFile", f"mkShapes__{tag}.root"), - "batchFolder": ns.get("batchFolder", "condor"), - } - - -# --------------------------------------------------------------------------- -# Workflow phases -# --------------------------------------------------------------------------- - -def phase1_submit(zptrw_dir, dry_run=False): - """Phase 1a+1b — compile and submit condor jobs.""" - banner("Phase 1a: Compile ZpTreweighting configuration") - rc = run_cmd(["mkShapesRDF", "-c", "1"], dry_run=dry_run, cwd=zptrw_dir) - if rc != 0: - sys.exit(f"ERROR: mkShapesRDF -c 1 failed (exit code {rc})") - - banner("Phase 1b: Submit ZpTreweighting condor jobs") - rc = run_cmd( - ["mkShapesRDF", "-o", "0", "-f", ".", "-b", "1"], - dry_run=dry_run, - cwd=zptrw_dir, - ) - if rc != 0: - sys.exit(f"ERROR: mkShapesRDF -o 0 failed (exit code {rc})") - - -def phase1_wait(zptrw_dir, cfg, poll_interval=120, dry_run=False): - """Phase 1c — wait for all condor jobs to finish.""" - banner("Phase 1c: Waiting for HTCondor jobs to complete") - batch_dir = os.path.join(zptrw_dir, cfg["batchFolder"]) - wait_for_condor_jobs( - batch_dir=batch_dir, - tag=cfg["tag"], - poll_interval=poll_interval, - dry_run=dry_run, - ) - - -def phase1_merge(zptrw_dir, dry_run=False): - """Phase 1d — merge individual job ROOT files (hadd).""" - banner("Phase 1d: Merge ROOT files") - rc = run_cmd( - ["mkShapesRDF", "-o", "2", "-f", "."], - dry_run=dry_run, - cwd=zptrw_dir, - ) - if rc != 0: - sys.exit(f"ERROR: Phase 1d - mkShapesRDF -o 2 (merge) failed (exit code {rc})") - - -def phase2_extract(zptrw_dir, cfg, year="2022", sample_type="LO", dry_run=False): - """Phase 2 — run extract_Zptrw.py to derive weights and update dyZpTrw.json.""" - banner("Phase 2: Extract Z pT reweighting function → update dyZpTrw.json") - - merged_file = os.path.join(cfg["outputFolder"], cfg["outputFile"]) - dyzptrw_json = os.path.join(zptrw_dir, "dyZpTrw.json") - extract_script = os.path.join(zptrw_dir, "extract_Zptrw.py") - - if not dry_run and not os.path.exists(merged_file): - sys.exit( - f"ERROR: Merged ROOT file not found:\n" - f" {merged_file}\n" - f"Run 'mkShapesRDF -o 2 -f .' in {zptrw_dir} first." - ) - - cmd = [ - sys.executable, extract_script, - "-f", - "--input", merged_file, - "--write-json", dyzptrw_json, - "--year", year, - "--sample-type", sample_type, - ] - rc = run_cmd(cmd, dry_run=dry_run, cwd=zptrw_dir) - if rc != 0: - sys.exit(f"ERROR: extract_Zptrw.py failed (exit code {rc})") - - info(f"dyZpTrw.json updated → {dyzptrw_json}") - - -def phase3_second_round(second_dir, dry_run=False): - """Phase 3 — compile and submit the second-round analysis.""" - banner(f"Phase 3: Second-round mkShapesRDF in\n {second_dir}") - - rc = run_cmd(["mkShapesRDF", "-c", "1"], dry_run=dry_run, cwd=second_dir) - if rc != 0: - sys.exit( - f"ERROR: mkShapesRDF -c 1 failed in {second_dir} (exit code {rc})" - ) - - rc = run_cmd( - ["mkShapesRDF", "-o", "0", "-f", ".", "-b", "1"], - dry_run=dry_run, - cwd=second_dir, - ) - if rc != 0: - sys.exit( - f"ERROR: mkShapesRDF -o 0 failed in {second_dir} (exit code {rc})" - ) - - -# --------------------------------------------------------------------------- -# CLI -# --------------------------------------------------------------------------- - -def parse_args(): - parser = argparse.ArgumentParser( - description=__doc__, - formatter_class=argparse.RawDescriptionHelpFormatter, - ) - - # ---- paths ---- - parser.add_argument( - "--zptrw-folder", - default=".", - metavar="DIR", - help="Path to the ZpTreweighting analysis folder " - "(default: current directory).", - ) - parser.add_argument( - "--second-analysis", - default=".", - metavar="DIR", - help="Path to the second-round analysis folder. " - "When given, Phase 3 compiles and submits jobs there after " - "dyZpTrw.py has been updated.", - ) - - # ---- physics ---- - parser.add_argument( - "--year", - default="2022", - help="Year key written to DYrew in dyZpTrw.json (default: '2022').", - ) - parser.add_argument( - "--sample-type", - default="LO", - help="Sample-type key written to DYrew in dyZpTrw.json " - "(default: 'LO').", - ) - - # ---- condor polling ---- - parser.add_argument( - "--poll-interval", - type=int, - default=120, - metavar="SECONDS", - help="Seconds between condor_q polls while waiting for jobs " - "(default: 120).", - ) - - # ---- skip flags ---- - skip = parser.add_argument_group("skip flags (for re-running partial workflow)") - skip.add_argument( - "--skip-submit", - action="store_true", - help="Skip Phase 1a+1b (assume jobs are already running or done).", - ) - skip.add_argument( - "--skip-wait", - action="store_true", - help="Skip Phase 1c (assume all jobs have already finished).", - ) - skip.add_argument( - "--skip-merge", - action="store_true", - help="Skip Phase 1d (assume the merged ROOT file already exists).", - ) - skip.add_argument( - "--skip-extract", - action="store_true", - help="Skip Phase 2 (assume dyZpTrw.json is already up to date).", - ) - - # ---- misc ---- - parser.add_argument( - "--dry-run", - action="store_true", - help="Print every command that would be run without executing it.", - ) - - return parser.parse_args() - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - -def main(): - args = parse_args() - - zptrw_dir = os.path.abspath(args.zptrw_folder) - if not os.path.isdir(zptrw_dir): - sys.exit(f"ERROR: ZpTreweighting folder not found: {zptrw_dir}") - - cfg_file = os.path.join(zptrw_dir, "configuration.py") - if not os.path.exists(cfg_file): - sys.exit(f"ERROR: configuration.py not found in {zptrw_dir}") - - banner("Z pT reweighting workflow") - info(f"ZpTreweighting folder : {zptrw_dir}") - if args.dry_run: - info("*** DRY-RUN mode — no commands will be executed ***") - - cfg = read_configuration(cfg_file) - info(f"tag : {cfg['tag']}") - info(f"outputFolder : {cfg['outputFolder']}") - info(f"outputFile : {cfg['outputFile']}") - info(f"batchFolder : {cfg['batchFolder']}") - - # ---- Phase 1a+1b: Submit ---- - if not args.skip_submit: - phase1_submit(zptrw_dir, dry_run=args.dry_run) - else: - info("\n[skip-submit] Skipping job submission.") - - # ---- Phase 1c: Wait ---- - if not args.skip_wait and not args.skip_submit: - phase1_wait( - zptrw_dir, - cfg, - poll_interval=args.poll_interval, - dry_run=args.dry_run, - ) - elif args.skip_wait: - info("\n[skip-wait] Skipping condor wait.") - - # ---- Phase 1d: Merge ---- - if not args.skip_merge: - phase1_merge(zptrw_dir, dry_run=args.dry_run) - else: - info("\n[skip-merge] Skipping ROOT file merge.") - - # ---- Phase 2: Extract + Update ---- - if not args.skip_extract: - phase2_extract( - zptrw_dir, - cfg, - year=args.year, - sample_type=args.sample_type, - dry_run=args.dry_run, - ) - else: - info("\n[skip-extract] Skipping weight extraction and dyZpTrw.json update.") - - # ---- Phase 3 (optional): Second analysis ---- - if args.second_analysis: - second_dir = os.path.abspath(args.second_analysis) - if not os.path.isdir(second_dir): - sys.exit(f"ERROR: Second-analysis folder not found: {second_dir}") - phase3_second_round(second_dir, dry_run=args.dry_run) - - banner("Workflow complete!") - if args.second_analysis: - info("Second-round jobs submitted. Next steps:") - info(f" Monitor : mkShapesRDF -o 1 -f {args.second_analysis}") - info(f" Merge : mkShapesRDF -o 2 -f {args.second_analysis}") - else: - info("dyZpTrw.json has been updated.") - info("To run the second-round analysis:") - info(" cd ") - info(" mkShapesRDF -c 1") - info(" mkShapesRDF -o 0 -f . -b 1") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/ZpTreweighting/dyZpTrw.py b/ZpTreweighting/dyZpTrw.py deleted file mode 100644 index 82f0ea2c..00000000 --- a/ZpTreweighting/dyZpTrw.py +++ /dev/null @@ -1,5 +0,0 @@ -DYrew={ - '2022': { - 'LO': "1.0133492539254507*0.843196*(0.211634*TMath::Erf((x-9.120552)/3.728196)-0.024628*x+0.000400*TMath::Sq(x)+1.361984)" - } -} \ No newline at end of file diff --git a/ZpTreweighting/readme.md b/ZpTreweighting/readme.md index 62d25ecd..6d643852 100644 --- a/ZpTreweighting/readme.md +++ b/ZpTreweighting/readme.md @@ -1,3 +1,9 @@ +## Deriving and applying ZpT reweights for all jet bin categories for a certain year: +Go within a certain config folder (divided by years) and run automate.py with required parameters values. Example: +`python3 automate.py --second-analysis ./ --year 2023 --sample-type NLO | tee automation_terminal_output.txt` + +It is advised to run the above codeline on a lingering screen (tmux or screen) owing to ~40 minutes of runtime (20 for each mkShapesRDF run), but not strictly necessary. + In order to produce the 2D histograms looking at gen_pTll vs pTll, an example runline(s) from the ZpTreweighting/ folder are as follows: ''' root From d5d1646ae7c3385aef94a2a015bb3e11fae116b1 Mon Sep 17 00:00:00 2001 From: CodexForster Date: Wed, 10 Jun 2026 01:23:28 -0500 Subject: [PATCH 12/13] PythonNN + TMVAbdt setup --- .../bdt_train/Classification_BDT.py | 375 ++++++++++++++++++ .../zh3l_run3_bdt/bdt_train/config_BDT.py | 36 ++ .../bdt_train/configuration_BDT.py | 17 + ZH3l_BDT/zh3l_run3_bdt/bdt_train/cuts_BDT.py | 144 +++++++ .../zh3l_run3_bdt/bdt_train/preselections.py | 17 + .../zh3l_run3_bdt/bdt_train/samples_BDT.py | 223 +++++++++++ ZH3l_BDT/zh3l_run3_bdt/bdt_train/structure.py | 38 ++ 7 files changed, 850 insertions(+) create mode 100644 ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py create mode 100644 ZH3l_BDT/zh3l_run3_bdt/bdt_train/config_BDT.py create mode 100644 ZH3l_BDT/zh3l_run3_bdt/bdt_train/configuration_BDT.py create mode 100644 ZH3l_BDT/zh3l_run3_bdt/bdt_train/cuts_BDT.py create mode 100644 ZH3l_BDT/zh3l_run3_bdt/bdt_train/preselections.py create mode 100644 ZH3l_BDT/zh3l_run3_bdt/bdt_train/samples_BDT.py create mode 100644 ZH3l_BDT/zh3l_run3_bdt/bdt_train/structure.py diff --git a/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py new file mode 100644 index 00000000..69abbc55 --- /dev/null +++ b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py @@ -0,0 +1,375 @@ +#!/usr/bin/env python +import ROOT +from ROOT import TMVA, TFile, TTree, TCut, TChain, RDataFrame +from subprocess import call +import os +from os.path import isfile +import json +import sys +import re +import numpy as np +import tensorflow as tf +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.metrics import roc_curve, roc_auc_score, accuracy_score, confusion_matrix +import matplotlib.pyplot as plt + +# import config_BDT as config +import preselections + +def make_model(input_dim): + model = tf.keras.Sequential([ + tf.keras.layers.Input(shape=(input_dim,)), + tf.keras.layers.Dense(128, activation="relu"), + tf.keras.layers.BatchNormalization(), + tf.keras.layers.Dropout(0.2), + tf.keras.layers.Dense(64, activation="relu"), + tf.keras.layers.BatchNormalization(), + tf.keras.layers.Dropout(0.2), + tf.keras.layers.Dense(32, activation="relu"), + tf.keras.layers.Dropout(0.1), + tf.keras.layers.Dense(1, activation="sigmoid") + ]) + + model.compile( + optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3), + loss="binary_crossentropy", + metrics=[ + tf.keras.metrics.AUC(name="auc"), + tf.keras.metrics.BinaryAccuracy(name="accuracy") + ] + ) + return model + + +def plot_and_save(y_true, y_score, outdir): + fpr, tpr, _ = roc_curve(y_true, y_score) + auc = roc_auc_score(y_true, y_score) + + plt.figure(figsize=(7, 6)) + plt.plot(fpr, tpr, lw=2, label=f"ROC AUC = {auc:.4f}") + plt.plot([0, 1], [0, 1], "--", color="gray") + plt.xlabel("Background efficiency") + plt.ylabel("Signal efficiency") + plt.title("DNN ROC curve") + plt.legend(loc="lower right") + plt.grid(True, alpha=0.3) + plt.tight_layout() + plt.savefig(os.path.join(outdir, "roc_curve.png"), dpi=200) + plt.close() + + plt.figure(figsize=(7, 6)) + plt.hist(y_score[y_true == 1], bins=50, histtype="step", density=True, label="Signal", linewidth=2) + plt.hist(y_score[y_true == 0], bins=50, histtype="step", density=True, label="Background", linewidth=2) + plt.xlabel("DNN score") + plt.ylabel("Normalized entries") + plt.title("DNN score distribution") + plt.legend() + plt.grid(True, alpha=0.3) + plt.tight_layout() + plt.savefig(os.path.join(outdir, "score_distribution.png"), dpi=200) + plt.close() + + return fpr, tpr, auc + +def alt_to_rdf(expr): + # Replace Alt$( X[n], d ) with (X.size() > n ? X[n] : d) + return re.sub( + r'Alt\$\(\s*([A-Za-z0-9_]+)\s*\[\s*(\d+)\s*\]\s*,\s*([^\)]+)\)', + r'(\1.size() > \2 ? \1[\2] : \3)', + expr + ) + +def build_dataframe(files, branches, cut_expr, label): + dfs = [] + for f in files: + df = RDataFrame("Events", f) # Reference - https://root.cern/doc/master/classROOT_1_1RDataFrame.html + # if cut_expr and str(cut_expr).strip(): + # cut_expr_rdf = alt_to_rdf(cut_expr) + # df = df.Filter(cut_expr_rdf) # Filter rows based on user-defined conditions + # # a C++ expression is passed to the Filter() operation as a string, even if we call the method from Python. + # cols = list(branches) + # Define scalar columns for array branches + df = df.Define("CleanJet_pt_0", "CleanJet_pt.size() > 0 ? CleanJet_pt[0] : 0") + df = df.Define("Lepton_pt_0", "Lepton_pt.size() > 0 ? Lepton_pt[0] : 0") + df = df.Define("Lepton_pt_1", "Lepton_pt.size() > 1 ? Lepton_pt[1] : 0") + df = df.Define("Lepton_pt_2", "Lepton_pt.size() > 2 ? Lepton_pt[2] : 0") + if cut_expr and str(cut_expr).strip(): + cut_expr_rdf = alt_to_rdf(cut_expr) + df = df.Filter(cut_expr_rdf) + # Use the defined scalar columns + cols = [ + "CleanJet_pt_0", + "ZH3l_dphilmetjj", + "PuppiMET_pt", + "Lepton_pt_0", + "Lepton_pt_1", + "Lepton_pt_2" + ] + rdf = df.AsNumpy(cols) # AsNumpy returns the columns of RDataFrame as a dict of numpy arrays + arr = np.column_stack([rdf[c] for c in cols]) + y = np.full((arr.shape[0], 1), label, dtype=np.int32) # the full function returns a new array of a given shape and data type, entirely filled with a specified value. + dfs.append((arr, y)) + if not dfs: + return np.empty((0, len(branches))), np.empty((0, 1), dtype=np.int32) + X = np.concatenate([d[0] for d in dfs], axis=0) + y = np.concatenate([d[1] for d in dfs], axis=0) + return X, y + +# Setup Tensorflow algorithm +def runJob_TF(output_and_dataset_name=""): + outdir = f"dataset{output_and_dataset_name}" + os.makedirs(outdir, exist_ok=True) + + # Load data + Xs, ys = [], [] + branches = list(config_mvaVariables_TF) + cuts = str(config_cut) + for sample_name, sample in samples.items(): + isData = structure[sample_name]["isData"] + # Don't train on data + if (isinstance(isData, int) and isData == 1) or (not isinstance(isData, int) and "all" in isData): + continue + # Create file list + files = [] + for entry in sample["name"]: + if isinstance(entry, (list, tuple)) and len(entry) >= 2: # translated from TMVA config file line 'for name, *location_weights in sample['name']:' + locations = entry[1] + for loc in locations: + files.append(loc) + + if len(files) == 0: + continue + label = 1 if structure[sample_name]["isSignal"] == 1 else 0 + X, y = build_dataframe(files, branches, cuts, label) + if X.shape[0] > 0: + Xs.append(X) + ys.append(y) + + if not Xs: + raise RuntimeError("No training data found. Check samples and file paths.") + + X = np.concatenate(Xs, axis=0).astype(np.float32) + y = np.concatenate(ys, axis=0).astype(np.int32).reshape(-1) + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + scaler = StandardScaler() # ensures that each feature has zero mean and unit variance + X_train = scaler.fit_transform(X_train) + # StandardScaler strictly uses the mean and variance calculated from the training data to transform both the training and testing datasets. It doesn't calculate new statistics for testing data, even if the testing data has different upper and lower limits -> comes with risk of data leakage, out-of-boud values, etc. + X_test = scaler.transform(X_test) + + np.save(os.path.join(outdir, "feature_names.npy"), np.array(branches, dtype=object)) + np.save(os.path.join(outdir, "X_train.npy"), X_train) + np.save(os.path.join(outdir, "X_test.npy"), X_test) + np.save(os.path.join(outdir, "y_train.npy"), y_train) + np.save(os.path.join(outdir, "y_test.npy"), y_test) + + model = make_model(X_train.shape[1]) + + callbacks = [ + tf.keras.callbacks.EarlyStopping( + monitor="val_auc", + patience=20, + mode="max", + restore_best_weights=True + ), + tf.keras.callbacks.ReduceLROnPlateau( + monitor="val_loss", + factor=0.5, + patience=10, + min_lr=1e-6 + ), + tf.keras.callbacks.ModelCheckpoint( + filepath=os.path.join(outdir, "best_model.keras"), + monitor="val_auc", + mode="max", + save_best_only=True + ) + ] + + history = model.fit( + X_train, y_train, + validation_split=0.2, + epochs=500, + batch_size=1024, + callbacks=callbacks, + verbose=2 + ) + + model.save(os.path.join(outdir, "final_model.keras")) + + y_score = model.predict(X_test, batch_size=4096).reshape(-1) + y_pred = (y_score >= 0.5).astype(np.int32) + + acc = accuracy_score(y_test, y_pred) + auc = roc_auc_score(y_test, y_score) + cm = confusion_matrix(y_test, y_pred) + + np.save(os.path.join(outdir, "y_score.npy"), y_score) + + metrics = { + "accuracy": float(acc), + "auc": float(auc), + "confusion_matrix": cm.tolist(), + "n_train": int(len(y_train)), + "n_test": int(len(y_test)), + "n_features": int(X_train.shape[1]) + } + + with open(os.path.join(outdir, "metrics.json"), "w") as f: + json.dump(metrics, f, indent=2) + + hist = history.history + np.savez( + os.path.join(outdir, "training_history.npz"), + loss=np.array(hist.get("loss", [])), + val_loss=np.array(hist.get("val_loss", [])), + auc=np.array(hist.get("auc", [])), + val_auc=np.array(hist.get("val_auc", [])), + accuracy=np.array(hist.get("accuracy", [])), + val_accuracy=np.array(hist.get("val_accuracy", [])) + ) + + fpr, tpr, roc_auc = plot_and_save(y_test, y_score, outdir) + + summary_path = os.path.join(outdir, "summary.txt") + with open(summary_path, "w") as f: + f.write(f"Accuracy: {acc:.6f}\n") + f.write(f"AUC: {auc:.6f}\n") + f.write(f"Train events: {len(y_train)}\n") + f.write(f"Test events: {len(y_test)}\n") + f.write(f"Features: {X_train.shape[1]}\n") + f.write("Confusion matrix:\n") + f.write(np.array2string(cm)) + + print(f"Saved outputs to: {outdir}") + print(f"Accuracy = {acc:.6f}") + print(f"AUC = {auc:.6f}") + +# Setup TMVA +def runJob_TMVA(output_and_dataset_name = ""): + TMVA.Tools.Instance() + # TMVA.PyMethodBase.PyInitialize() + + output = TFile.Open('TMVA{}.root'.format(output_and_dataset_name), 'RECREATE') + factory = TMVA.Factory('TMVAClassification', output,'!V:!Silent:Color:DrawProgressBar:AnalysisType=Classification') + # factory = TMVA.Factory('TMVAClassification', output,'!V:!Silent:Color:DrawProgressBar:Transformations=D,G:AnalysisType=Classification') + + dataloader = TMVA.DataLoader("dataset{}".format(output_and_dataset_name)) + + for br in config_mvaVariables: + dataloader.AddVariable(br) + + for sampleName, sample in samples.items(): + isData = structure[sampleName]['isData'] + if (isinstance(isData, int) and isData == 1) or (not isinstance(isData, int) and 'all' in isData): + continue + + sample['tree'] = TChain("Events") + print("Sample name: ", sampleName) + for name, *location_weights in sample['name']: + print("Sub-sample: ", name) + locations = location_weights[0] + # weights = location_weights[1] if len(location_weights) > 1 else None + for loc in locations: + print("file: ", loc) + sample['tree'].Add(loc) + + if structure[sampleName]['isSignal']==1: + dataloader.AddSignalTree(sample['tree'], 1.0) + else: + dataloader.AddBackgroundTree(sample['tree'], 1.0) + # output_dim += 1 + # Reference: https://root.cern.ch/download/doc/tmva/TMVAUsersGuide.pdf + # Train test dataset will contain less/equal events compared to signal and background trees. How these events are chosen is given by the next line. Event weights are given by Monte Carlo generators, and may turn out to be overall very small or large. To avoid artifacts due to this, TMVA can internally renormalise the signal and background training using NormMode. + dataloader.PrepareTrainingAndTestTree(TCut(config_cut),'SplitMode=Random:NormMode=NumEvents:!V') + # dataloader.PrepareTrainingAndTestTree(TCut(config.cut),'nTrain_Signal=100000:nTrain_Background=100000:SplitMode=Random:NormMode=NumEvents:!V')#SSSF + # Table 25 in TMVA UG explains all parameters, but to summarize: + # - NTrees: number of trees in forest, + # - nCuts: Number of grid points in variable range used in finding optimal cut in node splitting + # - MaxDepth: maximum depth of a tree allowed + # - MinNodeSize: Minimum percentage of training events required in a leaf node + # - BoostType: Boosting algorithm, here Gradient boosting + # - Shrinkage: learning rate + # - UseBaggedBoost: use bagging (Bootstrap AGGregatING) ... + factory.BookMethod(dataloader, TMVA.Types.kBDT, "gBDT_D2", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=2" ) + factory.BookMethod(dataloader, TMVA.Types.kBDT, "gBDT_D2_S01", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.01:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=2" ) + factory.BookMethod(dataloader, TMVA.Types.kBDT, "gBDT_D2_C300", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=300:MaxDepth=2" ) + factory.BookMethod(dataloader, TMVA.Types.kBDT, "gBDT_D3", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=3" ) + factory.BookMethod(dataloader, TMVA.Types.kBDT, "gBDT_D4", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=4" ) + factory.BookMethod(dataloader, TMVA.Types.kBDT, "gBDT_D5", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=5" ) + factory.BookMethod(dataloader, TMVA.Types.kBDT, "gBDT_D6", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.5:nCuts=500:MaxDepth=6" ) + # factory.BookMethod(dataloader, TMVA.Types.kBDT, "gBDT_D2_F07" , "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.7:nCuts=500:MaxDepth=2" ) + # factory.BookMethod(dataloader, TMVA.Types.kBDT, "gBDT_D2_S01_F07", "!H:!V:NTrees=500:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.01:UseBaggedBoost:GradBaggingFraction=0.7:nCuts=500:MaxDepth=2" ) + # Run training, test and evaluation + factory.TrainAllMethods() + factory.TestAllMethods() + factory.EvaluateAllMethods() + + output.Close() + +if __name__ == "__main__": + + print("Input arguments: {}".format(sys.argv)) + framework = "TF" # or "TMVA" + + isDEV=False + # Load configuration + with open("configuration_BDT.py") as handle: + exec(handle.read()) # Read the file content as a string + samples={} + structure={} + cuts={} + for f in [samplesFile, structureFile, cutsFile]: + with open(f) as handle: + exec(handle.read()) + + # Reduce sample files for fast dev + if isDEV: + for sampleName, sample in samples.items(): + if sampleName not in ['DY', 'top', 'ttV', 'WW', 'Zg', 'ZgS', 'WZ', 'ZZ', 'VVV', 'ZH_hww','ggZH_hww','WH_hww','ttH_hww', 'ZH_htt', 'WH_htt', 'Fake_e', 'Fake_m']: + # if sampleName not in ['Wg','Zg','WgS','ZgS','ZZ','WZ','top','DY','WH_hww_plus','WH_hww_minus','WH_htt_plus','WH_htt_minus']: + samples.pop(sampleName) + continue + + # Define data to be loaded + with open("./preselections.py") as handle: + exec(handle.read()) + + config_cut="(({0}) && ({1}))".format(cuts['NONE'],preselections['ALL']) + + config_mvaVariables = [ + 'Alt$( CleanJet_pt[0], 0)', + 'ZH3l_dphilmetjj', + 'PuppiMET_pt', + 'Alt$( Lepton_pt[0], 0)', + 'Alt$( Lepton_pt[1], 0)', + 'Alt$( Lepton_pt[2], 0)' + ] + + config_mvaVariables_TF = [ + "CleanJet_pt_0", + "ZH3l_dphilmetjj", + "PuppiMET_pt", + "Lepton_pt_0", + "Lepton_pt_1", + "Lepton_pt_2" + ] + + if len(sys.argv) > 1: + print("Suffix is: {}".format(sys.argv[1])) + output_and_dataset = sys.argv[1] + if framework == "TF": + runJob_TF(output_and_dataset) + elif framework == "TMVA": + runJob(output_and_dataset) + os.system("mv dataset dataset{}".format(output_and_dataset)) + else: + print("No suffix, running with standard output name") + if framework == "TF": + runJob_TF() + elif framework == "TMVA": + runJob() diff --git a/ZH3l_BDT/zh3l_run3_bdt/bdt_train/config_BDT.py b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/config_BDT.py new file mode 100644 index 00000000..58bc42a7 --- /dev/null +++ b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/config_BDT.py @@ -0,0 +1,36 @@ +#!/usr/bin/env python + +from __future__ import print_function +import os +from ROOT import gROOT, TFile, TChain, TCut + + + +# mvaVariables = [ +# 'CleanJet_pt[0][0]', +# 'ZH3l_dphilmetjj', +# 'PuppiMET_pt', +# 'Lepton_pt[0][0]', +# 'Lepton_pt[1][0]', +# 'Lepton_pt[2][0]' +# ] + +# : 1 : WH3l_ZVeto : 8.013e-02 +# : 2 : MinIf$(WH3l_mOSll[],WH3l_mOSll[Iteration$]>0) : 6.387e-02 +# : 3 : MinIf$(WH3l_ptOSll[],WH3l_ptOSll[Iteration$]>0) : 6.168e-02 +# : 4 : WH3l_dphilmet[0] : 5.700e-02 +# : 5 : Alt$(Lepton_pt[0],0) : 5.583e-02 +# : 6 : WH3l_dphilmet[2] : 5.460e-02 +# : 7 : WH3l_ptlll : 5.448e-02 +# : 8 : WH3l_dphilllmet : 5.393e-02 +# : 9 : WH3l_mtWWW : 5.162e-02 +# : 10 : WH3l_mlll : 5.103e-02 +# : 11 : WH3l_ptWWW : 5.083e-02 +# : 12 : MinIf$(WH3l_drOSll[],WH3l_drOSll[Iteration$]>0) : 4.966e-02 +# : 13 : PuppiMET_pt : 4.907e-02 +# : 14 : WH3l_mtlmet[1] : 4.747e-02 +# : 15 : WH3l_dphilmet[1] : 4.527e-02 +# : 16 : Alt$(Lepton_pt[1],0) : 4.456e-02 +# : 17 : WH3l_mtlmet[2] : 4.388e-02 +# : 18 : WH3l_mtlmet[0] : 4.260e-02 +# : 19 : Alt$(Lepton_pt[2],0) : 4.250e-02 diff --git a/ZH3l_BDT/zh3l_run3_bdt/bdt_train/configuration_BDT.py b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/configuration_BDT.py new file mode 100644 index 00000000..2c0c1027 --- /dev/null +++ b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/configuration_BDT.py @@ -0,0 +1,17 @@ +# example of configuration file + +import os + +tagName = '' + +# luminosity to normalize to (in 1/fb) +lumi = 8.0 + +# file with list of cuts +cutsFile = 'cuts_BDT.py' + +# file with list of samples +samplesFile = 'samples_BDT.py' + +# structure file for datacard +structureFile = 'structure.py' diff --git a/ZH3l_BDT/zh3l_run3_bdt/bdt_train/cuts_BDT.py b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/cuts_BDT.py new file mode 100644 index 00000000..7281a38a --- /dev/null +++ b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/cuts_BDT.py @@ -0,0 +1,144 @@ +# cuts + +cuts = {} +# 'MinIf$( WH3l_mOSll[], WH3l_mOSll[Iteration$] > 0) > 12 \ +# && Alt$(Lepton_pt[0],0)>25 \ +# && Alt$(Lepton_pt[1],0)>10 \ +# && Alt$(Lepton_pt[2],0)>10 \ +# && (nLepton>=3 && Alt$(Lepton_pt[3],0)<10) \ +# && abs(WH3l_chlll) == 1 \ +# && Alt$(CleanJet_pt[0], 0) < 30 \ +# && WH3l_flagOSSF == 1 \ +# && WH3l_ZVeto > 20 \ +# && PuppiMET_pt > 40 \ +# ' + +cuts['NONE'] = '1' # No cuts for BDT training + +cuts['zmass_cut'] = 'WH3l_ZVeto < 25' + +cuts['jet_cut_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + ' + +cuts['bveto_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + ' + +cuts['z4lveto_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + ' + +cuts['zh3l_SR_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetjj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetjj > 3.14159/2 \ + ' + +cuts['met_2j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + ' + +cuts['zh3l_SR_2j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetjj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_2j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) > 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetjj > 3.14159/2 \ + ' + +cuts['jet_cut_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + ' + +cuts['bveto_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + ' + +cuts['z4lveto_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + ' + +cuts['zh3l_SR_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && ZH3l_dphilmetj > 3.14159/2 \ + ' + +cuts['met_1j'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + ' + +cuts['zh3l_SR_1j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetj < 3.14159/2 \ + ' + +cuts['zh3l_WZ_CR_1j_met'] = ' Alt( CleanJet_pt, 0, 0) > 30 \ + && Alt( CleanJet_pt, 1, 0) < 30 \ + && WH3l_ZVeto < 25 \ + && bVeto \ + && ZH3l_Z4lveto > 20 \ + && PuppiMET_pt > 25 \ + && ZH3l_dphilmetj > 3.14159/2 \ + ' + +''' + #11 = e +# 13 = mu +# 15 = tau +''' diff --git a/ZH3l_BDT/zh3l_run3_bdt/bdt_train/preselections.py b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/preselections.py new file mode 100644 index 00000000..1eed05a0 --- /dev/null +++ b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/preselections.py @@ -0,0 +1,17 @@ +preselections = {} + +# preselections['ALL'] = 'Lepton_pt[0][0]>25 \ +# && Lepton_pt[1][0]>20 \ +# && Lepton_pt[2][0]>15 \ +# && Lepton_pt[3][0]<10 \ +# && (WH3l_mOSll[0] < 0 || WH3l_mOSll[0] > 12) \ +# && (WH3l_mOSll[1] < 0 || WH3l_mOSll[1] > 12) \ +# && (WH3l_mOSll[2] < 0 || WH3l_mOSll[2] > 12) \ +# && abs(WH3l_chlll) == 1 \ +# ' + +preselections['ALL'] = 'Alt$( Lepton_pt[0], 0) > 25 \ + && Alt$( Lepton_pt[1], 0) > 20 \ + && Alt$( Lepton_pt[2], 0) > 15 \ + && Alt$( Lepton_pt[3], 0) < 10 \ + ' \ No newline at end of file diff --git a/ZH3l_BDT/zh3l_run3_bdt/bdt_train/samples_BDT.py b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/samples_BDT.py new file mode 100644 index 00000000..a1327ae7 --- /dev/null +++ b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/samples_BDT.py @@ -0,0 +1,223 @@ +import os +import subprocess + +# global getSampleFiles +# from LatinoAnalysis.Tools.commonTools import getSampleFiles, addSampleWeight, getBaseWnAOD +# It looks like getBaseWnAOD and addSampleWeight from Latinos does the same job as CombineBaseW function from makeShapesRDF. Similarly getSampleFiles function from Latinos is the same as nanoGetSampleFiles from makeShapesRDF. + +from mkShapesRDF.lib.search_files import SearchFiles +searchFiles = SearchFiles() +redirector = "" +limitFiles = 1 +samples = {} + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + print("Found {} files for sample {}".format(len(_files), name)) + return [(name, _files[:limitFiles])] + else: + print("Found {} files for sample {}".format(len(_files), name)) + return [(name, _files)] + +def getSampleFilesNano(inputDir,Sample,absPath=False): + # return getSampleFiles(inputDir,Sample,absPath,'nanoLatino_') + return nanoGetSampleFiles(inputDir, Sample) + +def CombineBaseW(samples, proc, samplelist): + _filtFiles = list(filter(lambda k: k[0] in samplelist, samples[proc]["name"])) + _files = list(map(lambda k: k[1], _filtFiles)) + _l = list(map(lambda k: len(k), _files)) + leastFiles = _files[_l.index(min(_l))] + dfSmall = ROOT.RDataFrame("Runs", leastFiles) + s = dfSmall.Sum("genEventSumw").GetValue() + f = ROOT.TFile(leastFiles[0]) + t = f.Get("Events") + t.GetEntry(1) + xs = t.baseW * s + + __files = [] + for f in _files: + __files += f + df = ROOT.RDataFrame("Runs", __files) + s = df.Sum("genEventSumw").GetValue() + newbaseW = str(xs / s) + return newbaseW # "/baseW is used after getBaseWnAOD/CombineBaseW calls in this code" + # weight = newbaseW + "/baseW" + + # for iSample in samplelist: + # addSampleWeight(samples, proc, iSample, weight) + + +def addSampleWeight(samples, sampleName, sampleNameType, weight): + obj = list(filter(lambda k: k[0] == sampleNameType, samples[sampleName]["name"]))[0] + samples[sampleName]["name"] = list( + filter(lambda k: k[0] != sampleNameType, samples[sampleName]["name"]) + ) + if len(obj) > 2: + samples[sampleName]["name"].append( + (obj[0], obj[1], obj[2] + "*(" + weight + ")") + ) + else: + samples[sampleName]["name"].append((obj[0], obj[1], "(" + weight + ")")) + + +############################################## +###### Tree Directory according to site ###### +############################################## + +SITE=os.uname()[1] +xrootdPath='' +# xrootdPath='root://eoscms.cern.ch/' +treeBaseDir = '/eos/user/d/dshekar/MCsamplesForBDTzh3l/' +# treeBaseDir = '/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/' + +directory = treeBaseDir+'Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight' + +################################################ +############ NUMBER OF LEPTONS ################# +################################################ + +#Nlep='2' +Nlep='3' +#Nlep='4' + +################################################ +############### Lepton WP ###################### +################################################ + +eleWP='mvaFall17V1Iso_WP90' +#eleWP='mvaFall17V1Iso_WP90_SS' +#eleWP='mvaFall17V2Iso_WP90' +#eleWP='mvaFall17V2Iso_WP90_SS' +muWP ='cut_Tight_HWWW' +eleWP_new = 'cutBased_MediumID_tthMVA_Run3' +muWP_new = 'cut_TightID_pfIsoTight_HWW_tthmva_67' + + +LepWPCut = 'LepCut'+Nlep+'l__ele_'+eleWP_new+'__mu_'+muWP_new +#LepWPCut = 'LepCut'+Nlep+'l__ele_'+eleWP+'__mu_'+muWP +#LepWPweight = 'ttHMVA_SF_3l[0]' #SF for new WPs, defined in aliases +LepWPweight = 'LepSF'+Nlep+'l__ele_'+eleWP_new+'__mu_'+muWP_new + +################################################ +############ BASIC MC WEIGHTS ################## +################################################ + +XSWeight = 'XSWeight' +SFweight = 'SFweight'+Nlep+'l*'+LepWPweight+'*'+LepWPCut#+'*Jet_PUIDSF' +PromptGenLepMatch = 'PromptGenLepMatch'+Nlep+'l' + +################################################ +############## FAKE WEIGHTS #################### +################################################ + +#eleWP_new = 'mvaFall17V1Iso_WP90_tthmva_70' +#muWP_new = 'cut_Tight_HWWW_tthmva_80' + +if Nlep == '2' : + fakeW = 'fakeW2l_ele_'+eleWP_new+'_mu_'+muWP_new + #fakeW = 'fakeW2l_ele_'+eleWP+'_mu_'+muWP +else: + fakeW = 'fakeW_ele_'+eleWP_new+'_mu_'+muWP_new+'_'+Nlep+'l' + #fakeW = 'fakeW_ele_'+eleWP+'_mu_'+muWP+'_'+Nlep+'l' + +################################################ +############### B-Tag WP ###################### +################################################ + +SFweight += '*btagSF' #define in aliases.py + +################################################ +############ MET FILTERS ################### +################################################ + +METFilter_MC = 'METFilter_MC' +METFilter_DATA = 'METFilter_DATA' + +################################################ +############ DATA DECLARATION ################## +################################################ + +DataRun = [ + ['C','Run2022C-ReReco-v1'], + ['D','Run2022D-ReReco-v1'], +] + +DataSets = ['MuonEG','Muon','EGamma'] + +DataTrig = { + 'MuonEG' : 'Trigger_ElMu' , + 'Muon' : '!Trigger_ElMu && (Trigger_sngMu || Trigger_dblMu)', + 'EGamma' : '!Trigger_ElMu && !Trigger_sngMu && !Trigger_dblMu && (Trigger_sngEl || Trigger_dblEl)' +} + +########################################### +############# BACKGROUNDS ############### +########################################### + +############ DY ############ + +ptllDYW_NLO = '1' +ptllDYW_LO = '1' + +##### WZ + +samples['WZ'] = { 'name': getSampleFilesNano(directory,'WZTo3LNu') + + getSampleFilesNano(directory,'WZTo2L2Q') + + getSampleFilesNano(directory,'WZ'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC+'*(gstarHigh)' , + 'FilesPerJob' : 5 , + } + +samples['ZZ'] = { 'name' : getSampleFilesNano(directory,'ZZ'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC, + 'FilesPerJob' : 3, + } + +########################################## +################ SIGNALS ################# +########################################## + +############ ZH H->WW ############ + +# samples['ZH_hww'] = { 'name' : getSampleFilesNano(directory,'HZJ_HToWW_M125'), +# 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , +# } + +samples['ggZH_hww'] = { 'name' : getSampleFilesNano(directory,'GluGluZH_Zto2L_Hto2WtoLNu2Q'), + 'weight' : XSWeight+'*'+SFweight+'*'+PromptGenLepMatch+'*'+METFilter_MC , + } + +# directory = '/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2018_102X_nAODv7_Full2018v7/DATAl1loose2018v7__l2loose__fakeW/' + +############ +### DATA ### +############ + +samples['DATA'] = { + 'name': [], + 'weight': 'LepWPCut*METFilter_DATA', + 'weights': [], + 'isData': ['all'], + 'FilesPerJob': 15 +} + + +mcProduction = 'Summer22_130x_nAODv12_Full2022v12' +dataReco = 'Run2022_ReReco_nAODv12_Full2022v12' +dataSteps = 'DATAl2loose2022v12__l2loose' +treeBaseDir = '/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano' +dataDirectory = os.path.join(treeBaseDir, dataReco, dataSteps) + + +for _, sd in DataRun: + for pd in DataSets: + datatag = pd + '_' + sd + + files = nanoGetSampleFiles(dataDirectory, datatag) + + print(datatag) + + samples['DATA']['name'].extend(files) + addSampleWeight(samples, 'DATA', datatag, DataTrig[pd]) \ No newline at end of file diff --git a/ZH3l_BDT/zh3l_run3_bdt/bdt_train/structure.py b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/structure.py new file mode 100644 index 00000000..1291e9f2 --- /dev/null +++ b/ZH3l_BDT/zh3l_run3_bdt/bdt_train/structure.py @@ -0,0 +1,38 @@ +# structure configuration for datacard +# keys here must match keys in samples.py +structure = {} + +# Backgrounds + + +structure['WZ'] = { + 'isSignal' : 0, + 'isData' : 0 + } + +structure['ZZ'] = { + 'isSignal' : 0, + 'isData' : 0 + } + + +# Signal +# structure['ZH_hww'] = { +# 'isSignal' : 1, +# 'isData' : 0 +# } + +structure['ggZH_hww'] = { + 'isSignal' : 1, + 'isData' : 0 + } + +# Data +structure['DATA'] = { + 'isSignal' : 0, + 'isData' : 1 + } + + + + From 298e9f0609497ff16c9ecdead63bb247105368aa Mon Sep 17 00:00:00 2001 From: CodexForster Date: Tue, 23 Jun 2026 13:24:26 -0500 Subject: [PATCH 13/13] Truth plots for zh3l --- truth_plots_zh3l/nanoAODcontent.txt | 22333 ++++++++++++++++ truth_plots_zh3l/plotting.cpp | 322 + truth_plots_zh3l/plotting_ratio.c | 94 + truth_plots_zh3l/validation-genPlots.py | 277 + .../validation-recoPlots-cutFlow.py | 436 + truth_plots_zh3l/validation-recoPlots.py | 351 + .../validation-recoPlotsWhadDiJetSelection.py | 295 + 7 files changed, 24108 insertions(+) create mode 100644 truth_plots_zh3l/nanoAODcontent.txt create mode 100644 truth_plots_zh3l/plotting.cpp create mode 100644 truth_plots_zh3l/plotting_ratio.c create mode 100644 truth_plots_zh3l/validation-genPlots.py create mode 100644 truth_plots_zh3l/validation-recoPlots-cutFlow.py create mode 100644 truth_plots_zh3l/validation-recoPlots.py create mode 100644 truth_plots_zh3l/validation-recoPlotsWhadDiJetSelection.py diff --git a/truth_plots_zh3l/nanoAODcontent.txt b/truth_plots_zh3l/nanoAODcontent.txt new file mode 100644 index 00000000..acd0aa7c --- 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+*Entries : 251063 : Total Size= 1009341 bytes File Size = 12682 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 79.50 * +*............................................................................* +*Br 1910 :LepSF3l__mu_cut_TightID_POG__Up : LepSF3l__mu_cut_TightID_POG__Up/F* +*Entries : 251063 : Total Size= 1007781 bytes File Size = 11034 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 91.25 * +*............................................................................* +*Br 1911 :LepSF3l__ele_wp90iso__Down : LepSF3l__ele_wp90iso__Down/F * +*Entries : 251063 : Total Size= 1007631 bytes File Size = 10084 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 99.83 * +*............................................................................* +*Br 1912 :LepSF3l__mu_cut_TightID_POG__Down : * +* | LepSF3l__mu_cut_TightID_POG__Down/F * +*Entries : 251063 : Total Size= 1007841 bytes File Size = 11106 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 90.66 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Compression= 100.39 * +*............................................................................* +*Br 1916 :LepSF3l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67 : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67/F* +*Entries : 251063 : Total Size= 1009101 bytes File Size = 12478 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 80.78 * +*............................................................................* +*Br 1917 :LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_Tight_HWW : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_Tight_HWW/F* +*Entries : 251063 : Total Size= 1008741 bytes File Size = 12026 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 83.79 * +*............................................................................* +*Br 1918 :LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | 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:LepSF3l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_Tight_HWW : * +* | LepSF3l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_Tight_HWW/F * +*Entries : 251063 : Total Size= 1008651 bytes File Size = 11968 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 84.19 * +*............................................................................* +*Br 1936 :LepSF3l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF3l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 251063 : Total Size= 1009371 bytes File Size = 12684 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 79.49 * +*............................................................................* +*Br 1937 :LepSF3l__ele_cutBased_MediumID_tthMVA_HWW__Down : * +* | LepSF3l__ele_cutBased_MediumID_tthMVA_HWW__Down/F * +*Entries : 251063 : Total Size= 1008261 bytes File Size = 10602 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 95.01 * 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bytes File Size = 12588 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 80.09 * +*............................................................................* +*Br 1941 :LepSF3l__ele_mvaWinter22V2Iso_WP90__Up : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90__Up/F * +*Entries : 251063 : Total Size= 1007991 bytes File Size = 10384 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 96.98 * +*............................................................................* +*Br 1942 :LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_POG : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_POG/F* +*Entries : 251063 : Total Size= 1008831 bytes File Size = 12176 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 82.77 * +*............................................................................* +*Br 1943 :LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_Tight_HWW : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_Tight_HWW/F* 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:LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 251063 : Total Size= 1009461 bytes File Size = 12722 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 79.26 * +*............................................................................* +*Br 1947 :LepSF3l__ele_wp90iso__mu_cut_Tight_HWW : * +* | LepSF3l__ele_wp90iso__mu_cut_Tight_HWW/F * +*Entries : 251063 : Total Size= 1007991 bytes File Size = 11384 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 88.46 * +*............................................................................* +*Br 1948 :LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F* +*Entries : 251063 : Total Size= 1009461 bytes File Size = 12790 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 78.84 * +*............................................................................* +*Br 1949 :LepSF3l__ele_wp90iso__mu_cut_TightID_POG : * +* | LepSF3l__ele_wp90iso__mu_cut_TightID_POG/F * +*Entries : 251063 : Total Size= 1008051 bytes File Size = 11468 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 87.82 * +*............................................................................* +*Br 1950 :LepSF3l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_POG : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_POG/F * +*Entries : 251063 : Total Size= 1008471 bytes File Size = 11804 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 85.35 * +*............................................................................* +*Br 1951 :LepSF3l__ele_wp90iso__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67 : * +* | LepSF3l__ele_wp90iso__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67/F * +*Entries : 251063 : Total Size= 1008681 bytes File Size = 12070 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 83.48 * +*............................................................................* +*Br 1952 :LepSF3l__ele_testrecipes__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF3l__ele_testrecipes__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F* +*Entries : 251063 : Total Size= 1008801 bytes File Size = 12202 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 82.59 * +*............................................................................* +*Br 1953 :LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_POG : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_POG/F* +*Entries : 251063 : Total Size= 1008801 bytes File Size = 12142 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 83.00 * +*............................................................................* +*Br 1954 :LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67 : * +* | 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+*............................................................................* +*Br 1957 :LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__Up : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__Up/F * +*Entries : 251063 : Total Size= 1008351 bytes File Size = 10688 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 94.25 * +*............................................................................* +*Br 1958 :LepSF3l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW__Up : * +* | LepSF3l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW__Up/F * +*Entries : 251063 : Total Size= 1008441 bytes File Size = 11646 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 86.50 * +*............................................................................* +*Br 1959 :LepSF3l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW__Down : * +* | LepSF3l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW__Down/F * +*Entries : 251063 : Total Size= 1008501 bytes File Size = 11758 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 85.68 * +*............................................................................* +*Br 1960 :LepSF3l__ele_testrecipes__mu_cut_Tight_HWW : * +* | LepSF3l__ele_testrecipes__mu_cut_Tight_HWW/F * +*Entries : 251063 : Total Size= 1008111 bytes File Size = 11500 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 87.58 * +*............................................................................* +*Br 1961 :LepSF3l__mu_cut_TightID_pfIsoTight_HWW_tthmva_67__Down : * +* | LepSF3l__mu_cut_TightID_pfIsoTight_HWW_tthmva_67__Down/F * +*Entries : 251063 : Total Size= 1008471 bytes File Size = 11772 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 85.58 * +*............................................................................* +*Br 1962 :LepSF3l__ele_testrecipes__mu_cut_TightID_POG : * +* | LepSF3l__ele_testrecipes__mu_cut_TightID_POG/F * +*Entries : 251063 : Total Size= 1008171 bytes File Size = 11612 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 86.74 * +*............................................................................* +*Br 1963 :LepSF3l__ele_cutBased_MediumID_tthMVA_HWW__Up : * +* | LepSF3l__ele_cutBased_MediumID_tthMVA_HWW__Up/F * +*Entries : 251063 : Total Size= 1008201 bytes File Size = 10554 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 95.43 * +*............................................................................* +*Br 1964 :LepSF3l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67__Down : * +* | LepSF3l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67__Down/F * +*Entries : 251063 : Total Size= 1008471 bytes File Size = 11768 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 85.61 * +*............................................................................* +*Br 1965 :LepSF3l__mu_cut_Tight_HWW__Up : LepSF3l__mu_cut_Tight_HWW__Up/F * +*Entries : 251063 : Total Size= 1007721 bytes File Size = 11042 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 91.18 * +*............................................................................* +*Br 1966 :LepSF3l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_POG : * +* | LepSF3l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_POG/F * +*Entries : 251063 : Total Size= 1008711 bytes File Size = 12044 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 83.66 * +*............................................................................* +*Br 1967 :LepSF4l__mu_cut_TightID_pfIsoTight_HWW_tthmva_67__Down : * +* | LepSF4l__mu_cut_TightID_pfIsoTight_HWW_tthmva_67__Down/F * +*Entries : 251063 : Total Size= 1008471 bytes File Size = 6996 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 144.00 * +*............................................................................* +*Br 1968 :LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F* +*Entries : 251063 : Total Size= 1009311 bytes File Size = 7724 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 130.53 * +*............................................................................* +*Br 1969 :LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 251063 : Total Size= 1008831 bytes File Size = 7308 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 137.90 * +*............................................................................* +*Br 1970 :LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 251063 : Total Size= 1009371 bytes File Size = 7776 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 129.66 * +*............................................................................* +*Br 1971 :LepSF4l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67__Up : * +* | LepSF4l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67__Up/F * +*Entries : 251063 : Total Size= 1008411 bytes File Size = 6940 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 145.16 * +*............................................................................* +*Br 1972 :LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__mu_cut_Tight_HWW : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__mu_cut_Tight_HWW/F * +*Entries : 251063 : Total Size= 1008621 bytes File Size = 7126 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 141.39 * +*............................................................................* +*Br 1973 :LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_POG : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_POG/F * +*Entries : 251063 : Total Size= 1008471 bytes File Size = 6996 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 144.00 * +*............................................................................* +*Br 1974 :LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67 : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67/F* +*Entries : 251063 : Total Size= 1009341 bytes File Size = 7750 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 130.09 * +*............................................................................* +*Br 1975 :LepSF4l__ele_mvaWinter22V2Iso_WP90__Down : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90__Down/F * +*Entries : 251063 : Total Size= 1008051 bytes File Size = 6620 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 152.13 * +*............................................................................* +*Br 1976 :LepSF4l__ele_wp90iso__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF4l__ele_wp90iso__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F * +*Entries : 251063 : Total Size= 1008681 bytes File Size = 7178 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 140.38 * +*............................................................................* +*Br 1977 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_POG : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_POG/F* +*Entries : 251063 : Total Size= 1008831 bytes File Size = 7308 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 137.90 * +*............................................................................* +*Br 1978 :LepSF4l__ele_testrecipes__Down : LepSF4l__ele_testrecipes__Down/F * +*Entries : 251063 : Total Size= 1007751 bytes File Size = 6360 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 158.31 * +*............................................................................* +*Br 1979 :LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__Down : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__Down/F * +*Entries : 251063 : Total Size= 1008291 bytes File Size = 6828 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 147.52 * +*............................................................................* +*Br 1980 :LepSF4l__mu_cut_TightID_POG__Up : LepSF4l__mu_cut_TightID_POG__Up/F* +*Entries : 251063 : Total Size= 1007781 bytes File Size = 6398 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 157.37 * +*............................................................................* +*Br 1981 :LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F* +*Entries : 251063 : Total Size= 1009341 bytes File Size = 7750 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 130.09 * +*............................................................................* +*Br 1982 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 251063 : Total Size= 1009491 bytes File Size = 7880 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 127.96 * +*............................................................................* +*Br 1983 :LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 251063 : Total Size= 1009131 bytes File Size = 7568 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 133.20 * +*............................................................................* +*Br 1984 :LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_Tight_HWW : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_Tight_HWW/F * +*Entries : 251063 : Total Size= 1008411 bytes File Size = 6944 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 145.07 * +*............................................................................* +*Br 1985 :LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67 : * +* | LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67/F* +*Entries : 251063 : Total Size= 1008801 bytes File Size = 7282 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 138.39 * +*............................................................................* +*Br 1986 :LepSF4l__ele_mvaWinter22V2Iso_WP90__Up : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90__Up/F * +*Entries : 251063 : Total Size= 1007991 bytes File Size = 6568 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 153.32 * +*............................................................................* +*Br 1987 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F* +*Entries : 251063 : Total Size= 1009431 bytes File Size = 7828 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 128.80 * +*............................................................................* +*Br 1988 :LepSF4l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67__Down : * +* | LepSF4l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67__Down/F * +*Entries : 251063 : Total Size= 1008471 bytes File Size = 6996 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 144.00 * +*............................................................................* +*Br 1989 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_Tight_HWW : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_Tight_HWW/F* +*Entries : 251063 : Total Size= 1008771 bytes File Size = 7256 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 138.88 * +*............................................................................* +*Br 1990 :LepSF4l__mu_cut_Tight_HWW__Down : LepSF4l__mu_cut_Tight_HWW__Down/F* +*Entries : 251063 : Total Size= 1007781 bytes File Size = 6398 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 157.37 * +*............................................................................* +*Br 1991 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_POG : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_POG/F* +*Entries : 251063 : Total Size= 1008801 bytes File Size = 7282 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 138.39 * +*............................................................................* +*Br 1992 :LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__mu_cut_TightID_POG : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__mu_cut_TightID_POG/F * +*Entries : 251063 : Total Size= 1008681 bytes File Size = 7178 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 140.38 * +*............................................................................* +*Br 1993 :LepSF4l__ele_wp90iso__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF4l__ele_wp90iso__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F * +*Entries : 251063 : Total Size= 1008711 bytes File Size = 7204 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 139.87 * +*............................................................................* +*Br 1994 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 251063 : Total Size= 1009461 bytes File Size = 7854 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 128.38 * +*............................................................................* +*Br 1995 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__Down : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__Down/F * +*Entries : 251063 : Total Size= 1008381 bytes File Size = 6906 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 145.87 * +*............................................................................* +*Br 1996 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_Tight_HWW : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_Tight_HWW/F* +*Entries : 251063 : Total Size= 1008741 bytes File Size = 7230 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 139.38 * +*............................................................................* +*Br 1997 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__Up : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__Up/F * +*Entries : 251063 : Total Size= 1008351 bytes File Size = 6880 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 146.42 * +*............................................................................* +*Br 1998 :LepSF4l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW__Up : * +* | LepSF4l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW__Up/F * +*Entries : 251063 : Total Size= 1008441 bytes File Size = 6970 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 144.54 * +*............................................................................* +*Br 1999 :LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F* +*Entries : 251063 : Total Size= 1008801 bytes File Size = 7282 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 138.39 * +*............................................................................* +*Br 2000 :LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F* +*Entries : 251063 : Total Size= 1009101 bytes File Size = 7542 * +*Baskets : 26 : Basket Size= 32000 bytes Compression= 133.65 * +*............................................................................* +*Br 2001 :LepSF4l__ele_testrecipes__Up : LepSF4l__ele_testrecipes__Up/F * +*Entries : 251063 : Total Size= 1007691 bytes File 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+(myenv) [dshekar@lxplus941 mkShapesRDF_temp]$ exit +logout +Connection to lxplus.cern.ch closed. +danush@macbookpro ZpTreweighting % + [Restored May 4, 2026 at 20:32:36] +Last login: Mon May 4 20:32:26 on console +Restored session: Mon May 4 20:31:31 CDT 2026 +danush@danush-5 ZpTreweighting % lxplus +(dshekar@lxplus.cern.ch) Password: +Connection closed by 188.184.108.43 port 22 +danush@danush-5 ZpTreweighting % lxplus +(dshekar@lxplus.cern.ch) Password: +(dshekar@lxplus.cern.ch) Your 2nd factor (dshekar): 242832 +* ******************************************************************** +* Welcome to lxplus946.cern.ch, Red Hat Enterprise Linux release 9.7 (Plow) +* Archive of news is available in /etc/motd-archive +* Reminder: you have agreed to the CERN +* computing rules, in particular OC5. CERN implements +* the measures necessary to ensure compliance. +* https://cern.ch/ComputingRules +* Puppet environment: production, Roger state: production +* Foreman hostgroup: lxplus/nodes/login +* Availability zone: cern-geneva-c +* LXPLUS Public Login Service - http://lxplusdoc.web.cern.ch/ +* Please read LXPLUS Privacy Notice in http://cern.ch/go/TpV7 +* ******************************************************************** +* /!\ LxPlus now requires 2FA from outside of CERN /!\ +* All details for 2FA enforcing on https://cern.ch/otg0156449 + +[dshekar@lxplus946 ~]$ ssh dpgtkdqm@vocms065 +(dpgtkdqm@vocms065) Password: +* ******************************************************************** +* Welcome to vocms065.cern.ch, Red Hat Enterprise Linux release 9.7 (Plow) +* Archive of news is available in /etc/motd-archive +* Reminder: you have agreed to the CERN +* computing rules, in particular OC5. CERN implements +* the measures necessary to ensure compliance. +* https://cern.ch/ComputingRules +* Puppet environment: production, Roger state: build +* Foreman hostgroup: vocms/tracker/dqm +* Availability zone: cern-geneva-c +* VOCMS cluster node +* Responsible: cms-voc@cern.ch +* ******************************************************************** +Last login: Mon May 4 23:01:32 2026 from 2001:1458:d00:2d::100:26d +[dpgtkdqm@vocms065 ~]$ source badComponentsTrend.sh Express 2026 403044 403493 +CMSSW area:/data/users/event_display/dpgtkdqm/remotescripts/BadComponentsTrend/CMSSW_14_0_14/src +Exec area:/data/users/event_display/dpgtkdqm/remotescripts/BadComponentsTrend +Dataset: Express - Year : 2026 - Start Run: 403044 - End Run : 403493 +------------------------------------------------------------------------ +SiStrip Bad Components plot generation +/eos/cms/store/group/tracker-cctrack/www/HDQM/v4/alljsons/2026/StreamExpress/Strips/DECO/ +{-999: 0, 11616: 2, 11618: 3, 11620: 6, 11621: 8, 11625: 10, 11627: 13, 11631: 15, 11643: 16, 11644: 17, 11660: 18, 11661: 20, 11662: 21, 11666: 22, 11667: 23, 11669: 25, 11670: 28, 11671: 31, 11672: 32, 11674: 34, 11675: 35} +Number of Bins: 35 +Global Run: 35 +TIB Run: 35 +TID Run: 35 +TOB Run: 35 +TEC Run: 35 +TIB mean=318765.039511277 +TID mean=306415.79561145604 +291972.53472692054 +289150.519732045 +Info in : pdf file /eos/cms/store/group/tracker-cctrack/www/TrackerMapsReloaded/files/data/users/event_display/BadComponentsTrends//SiStripBadComponents_Express_403091-403493.pdf has been created +Info in : png file current/SiStripBadComponents.png has been created +output files can be found on https://tkmaps.web.cern.ch/tkmaps/files/data/users/event_display/BadComponentsTrends/ +------------------------------------------------------------------------ +Pixel Dead ROC plot generation +/eos/cms/store/group/tracker-cctrack/www/HDQM/v4/alljsons/2026/StreamExpress/PixelPhase1/ +{-999: 0, 11616: 2, 11618: 3, 11620: 6, 11621: 8, 11625: 10, 11627: 13, 11631: 15, 11643: 16, 11644: 17, 11660: 18, 11661: 20, 11662: 21, 11666: 22, 11667: 23, 11669: 25, 11670: 28, 11671: 31, 11672: 32, 11674: 34, 11675: 35} +Number of Bins: 35 +BPix L1 Run: 35 +BPix L2 Run: 35 +BPix L3 Run: 35 +BPix L4 Run: 35 +FPix R1 Run: 35 +FPix R2 Run: 35 +Info in : pdf file /eos/cms/store/group/tracker-cctrack/www/TrackerMapsReloaded/files/data/users/event_display/BadComponentsTrends//PixelBadComponents_Express_403091-403493.pdf has been created +Info in : png file current/PixelBadComponents.png has been created +output files can be found on https://tkmaps.web.cern.ch/tkmaps/files/data/users/event_display/BadComponentsTrends/ +[dpgtkdqm@vocms065 ~]$ source badComponentsTrend.sh Prompt 2026 402982 403420 +CMSSW area:/data/users/event_display/dpgtkdqm/remotescripts/BadComponentsTrend/CMSSW_14_0_14/src +Exec area:/data/users/event_display/dpgtkdqm/remotescripts/BadComponentsTrend +Dataset: Prompt - Year : 2026 - Start Run: 402982 - End Run : 403420 +------------------------------------------------------------------------ +SiStrip Bad Components plot generation +/eos/cms/store/group/tracker-cctrack/www/HDQM/v4/alljsons/2026/Prompt/ZeroBias/Strips/DECO/ +{-999: 0, 11601: 1, 11602: 3, 11604: 4, 11616: 6, 11618: 7, 11620: 10, 11621: 12, 11625: 14, 11627: 17, 11631: 19, 11643: 20, 11644: 21, 11660: 22, 11661: 24, 11662: 25, 11666: 26, 11667: 27, 11669: 28} +Number of Bins: 28 +Global Run: 28 +TIB Run: 28 +TID Run: 28 +TOB Run: 28 +TEC Run: 28 +TIB mean=330148.70477039076 +TID mean=346793.97545211465 +362465.3793827287 +366123.7774050469 +Info in : pdf file /eos/cms/store/group/tracker-cctrack/www/TrackerMapsReloaded/files/data/users/event_display/BadComponentsTrends//SiStripBadComponents_Prompt_403008-403420.pdf has been created +Info in : png file current/SiStripBadComponents.png has been created +output files can be found on https://tkmaps.web.cern.ch/tkmaps/files/data/users/event_display/BadComponentsTrends/ +------------------------------------------------------------------------ +Pixel Dead ROC plot generation +/eos/cms/store/group/tracker-cctrack/www/HDQM/v4/alljsons/2026/Prompt/ZeroBias/PixelPhase1/ +{-999: 0, 11601: 1, 11602: 3, 11604: 4, 11616: 6, 11618: 7, 11620: 10, 11621: 12, 11625: 14, 11627: 17, 11631: 19, 11643: 20, 11644: 21, 11660: 22, 11661: 24, 11662: 25, 11666: 26, 11667: 27, 11669: 28} +Number of Bins: 28 +BPix L1 Run: 28 +BPix L2 Run: 28 +BPix L3 Run: 28 +BPix L4 Run: 28 +FPix R1 Run: 28 +FPix R2 Run: 28 +Info in : pdf file /eos/cms/store/group/tracker-cctrack/www/TrackerMapsReloaded/files/data/users/event_display/BadComponentsTrends//PixelBadComponents_Prompt_403008-403420.pdf has been created +Info in : png file current/PixelBadComponents.png has been created +output files can be found on https://tkmaps.web.cern.ch/tkmaps/files/data/users/event_display/BadComponentsTrends/ +[dpgtkdqm@vocms065 ~]$ source badComponentsTrend.sh Express 2026 403044 403493Read from remote host lxplus.cern.ch: Operation timed out +Connection to lxplus.cern.ch closed. +client_loop: send disconnect: Broken pipe +danush@danush-5 ZpTreweighting % lxplus +(dshekar@lxplus.cern.ch) Password: +(dshekar@lxplus.cern.ch) Your 2nd factor (dshekar): 777473 +* ******************************************************************** +* Welcome to lxplus980.cern.ch, Red Hat Enterprise Linux release 9.7 (Plow) +* Archive of news is available in /etc/motd-archive +* Reminder: you have agreed to the CERN +* computing rules, in particular OC5. CERN implements +* the measures necessary to ensure compliance. +* https://cern.ch/ComputingRules +* Puppet environment: qa, Roger state: production +* Foreman hostgroup: lxplus/nodes/login +* Availability zone: cern-geneva-b +* LXPLUS Public Login Service - http://lxplusdoc.web.cern.ch/ +* Please read LXPLUS Privacy Notice in http://cern.ch/go/TpV7 +* ******************************************************************** +* /!\ LxPlus now requires 2FA from outside of CERN /!\ +* All details for 2FA enforcing on https://cern.ch/otg0156449 + +[dshekar@lxplus980 ~]$ ls +__MACOSX private public +[dshekar@lxplus980 ~]$ cd public/ +[dshekar@lxplus980 public]$ ls +LNuQQ mcPlots p2dpg RDF x509up_u154072 +[dshekar@lxplus980 public]$ cd ../private/ +[dshekar@lxplus980 private]$ ls +baby-moss-sw baby-moss-sw-job github github.pub __MACOSX mcSamples semiparametric-master x509up_u154072 +[dshekar@lxplus980 private]$ cd ../public/RDF/ +[dshekar@lxplus980 RDF]$ ls +Latinos mkShapesRDF mkShapesRDF_OLD mkShapesRDF_postproc mkShapesRDF_temp PlotsConfigurationsRun3 PlotsConfigurationsRun3_fork +[dshekar@lxplus980 RDF]$ cd mkShapesRDF_temp/ +[dshekar@lxplus980 mkShapesRDF_temp]$ grep -ril "amassiro" +.git/packed-refs +docs/shapes/python/samples.py +examples/2016Real/samples.py +examples/Full2018_v9/samples.py +examples/test_folder/samples.py +mkShapesRDF/processor/framework/Sites_cfg.py +mkShapesRDF/processor/framework/__pycache__/Sites_cfg.cpython-313.pyc +mkShapesRDF/processor/scripts/examples.txt +mkShapesRDF/processor/scripts/mkPostProc.py +mkShapesRDF/processor/condor/Summer23BPix_130x_nAODv12_Full2023BPixv12/MCl2loose2023BPixv12__MCCorr2023BPixv12JetScaling__l2tight/WWW__part0/script.py +mkShapesRDF/processor/condor/Summer23BPix_130x_nAODv12_Full2023BPixv12/MCl2loose2023BPixv12__MCCorr2023BPixv12JetScaling__l2tight/WWW__part1/script.py 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+mkShapesRDF/processor/condor/Summer23BPix_130x_nAODv12_Full2023BPixv12/MCl2loose2023BPixv12__MCCorr2023BPixv12JetScaling__l2tight/GluGlutoContintoWWtoTauNuTauNu__part8/script.py +mkShapesRDF/shapeAnalysis/latinos/PlotFactory.py +[dshekar@lxplus980 mkShapesRDF_temp]$ vim mkShapesRDF/processor/scripts/mkPostProc.py +[dshekar@lxplus980 mkShapesRDF_temp]$ vim mkShapesRDF/shapeAnalysis/latinos/PlotFactory.py +[dshekar@lxplus980 mkShapesRDF_temp]$ vim mkShapesRDF/processor/framework/Sites_cfg.py +[dshekar@lxplus980 mkShapesRDF_temp]$ vim mkShapesRDF/processor/scripts/examples.txt +[dshekar@lxplus980 mkShapesRDF_temp]$ vim mkShapesRDF/processor/scripts/mkPostProc.py +[dshekar@lxplus980 mkShapesRDF_temp]$ cd .. +[dshekar@lxplus980 RDF]$ ls +Latinos mkShapesRDF mkShapesRDF_OLD mkShapesRDF_postproc mkShapesRDF_temp PlotsConfigurationsRun3 PlotsConfigurationsRun3_fork +[dshekar@lxplus980 RDF]$ ls -ltr +total 14 +drwxr-xr-x. 15 dshekar zh 2048 Sep 17 2025 PlotsConfigurationsRun3_fork +drwxr-xr-x. 2 dshekar zh 2048 Dec 18 21:08 Latinos +drwxr-xr-x. 19 dshekar zh 2048 Apr 13 23:02 PlotsConfigurationsRun3 +drwxr-xr-x. 11 dshekar zh 2048 Apr 22 02:45 mkShapesRDF_OLD +drwxr-xr-x. 11 dshekar zh 2048 Apr 22 22:42 mkShapesRDF +drwxr-xr-x. 11 dshekar zh 2048 Apr 25 04:28 mkShapesRDF_postproc +drwxr-xr-x. 11 dshekar zh 2048 Apr 27 17:48 mkShapesRDF_temp +[dshekar@lxplus980 RDF]$ rm -r mkShapesRDF_postproc +rm: remove write-protected regular file 'mkShapesRDF_postproc/.git/objects/pack/pack-4b2f59727c5e2fe7eab00a95b9a868801cfd943e.pack'? ^C +[dshekar@lxplus980 RDF]$ rm -rf mkShapesRDF_postproc +[dshekar@lxplus980 RDF]$ ls +Latinos mkShapesRDF mkShapesRDF_OLD mkShapesRDF_temp PlotsConfigurationsRun3 PlotsConfigurationsRun3_fork +[dshekar@lxplus980 RDF]$ mv mkShapesRDF_temp/ mkShapesRDF_postproc +[dshekar@lxplus980 RDF]$ ls -ltr +total 12 +drwxr-xr-x. 15 dshekar zh 2048 Sep 17 2025 PlotsConfigurationsRun3_fork +drwxr-xr-x. 2 dshekar zh 2048 Dec 18 21:08 Latinos +drwxr-xr-x. 19 dshekar zh 2048 Apr 13 23:02 PlotsConfigurationsRun3 +drwxr-xr-x. 11 dshekar zh 2048 Apr 22 02:45 mkShapesRDF_OLD +drwxr-xr-x. 11 dshekar zh 2048 Apr 22 22:42 mkShapesRDF +drwxr-xr-x. 11 dshekar zh 2048 Apr 27 17:48 mkShapesRDF_postproc +[dshekar@lxplus980 RDF]$ cd mkShapesRDF_OLD/ +[dshekar@lxplus980 mkShapesRDF_OLD]$ ls +Dockerfile docs environment.yml examples install.sh MANIFEST.in mkShapesRDF mkShapesRDF.egg-info myenv pyproject.toml README.md setup.cfg start.sh tests utils +[dshekar@lxplus980 mkShapesRDF_OLD]$ vim mkShapesRDF/processor/ +condor/ data/ framework/ __init__.py modules/ __pycache__/ scripts/ +[dshekar@lxplus980 mkShapesRDF_OLD]$ vim mkShapesRDF/processor/framework/S +Sites_cfg.py Steps_cfg.py +[dshekar@lxplus980 mkShapesRDF_OLD]$ vim mkShapesRDF/processor/framework/Sites_cfg.py +[dshekar@lxplus980 mkShapesRDF_OLD]$ cd .. +[dshekar@lxplus980 RDF]$ cd mkShapesRDF/mkShapesRDF/processor/framework/ +[dshekar@lxplus980 framework]$ vim Sites_cfg.py +[dshekar@lxplus980 framework]$ cd . +[dshekar@lxplus980 framework]$ cd .. +[dshekar@lxplus980 processor]$ cd .. +[dshekar@lxplus980 mkShapesRDF]$ cd .. +[dshekar@lxplus980 mkShapesRDF]$ ls +Dockerfile docs environment.yml examples install.sh MANIFEST.in mkShapesRDF mkShapesRDF.egg-info myenv pyproject.toml README.md setup.cfg start.sh tests utils +[dshekar@lxplus980 mkShapesRDF]$ cd .. +[dshekar@lxplus980 RDF]$ ls +Latinos mkShapesRDF mkShapesRDF_OLD mkShapesRDF_postproc PlotsConfigurationsRun3 PlotsConfigurationsRun3_fork +[dshekar@lxplus980 RDF]$ ls +Latinos mkShapesRDF mkShapesRDF_OLD mkShapesRDF_postproc PlotsConfigurationsRun3 PlotsConfigurationsRun3_fork +[dshekar@lxplus980 RDF]$ rm -r mkShapesRDF_OLD/ +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/pack/pack-115e8fd6d36323c6ec241c08360fca279ef4d1ff.pack'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/pack/pack-115e8fd6d36323c6ec241c08360fca279ef4d1ff.rev'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/pack/pack-115e8fd6d36323c6ec241c08360fca279ef4d1ff.idx'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/pack/pack-846aa40b017f11a588e7ccf24acc5de1c2139c49.pack'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/pack/pack-846aa40b017f11a588e7ccf24acc5de1c2139c49.rev'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/pack/pack-846aa40b017f11a588e7ccf24acc5de1c2139c49.idx'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/fc/29e32c6719458659828d0f0f28c02a2c037991'? y +yrm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/13/8848fce6dd579c60792dcbc7ab45d573e851b1'? +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/25/c2b5f9d72a8d7ea057ffd021e8040381362655'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/6e/c7c025f729266e93135d90de693ce26963edd4'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/8b/71567704403b710867719a0e17ebae6d8ee6c4'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/d2/8202a968cff5e75041e5887ee37816e3513758'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/a9/0c97624edb46c56aa821a380952221b026e8a2'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/79/27b600c6ed2518439992372acacfe774e5f607'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/39/ba4350430c002637ff7f68d075149f99b42af6'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/08/81b49f09868f94e5c29b0fc6ff712898053053'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/6b/546334d64295d5ed8b0d403c6416638449b9bd'? y +yrm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/51/76f43d9d99391b4b5231070eee870be3f81c85'? +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/07/5176236f0575a2acd7236b61e5e920116eaf3e'? y +rm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/a2/6330684e589eaac09ea8c2fc5b1a4f474240b3'? y +yrm: remove write-protected regular file 'mkShapesRDF_OLD/.git/objects/84/5d0e20a9b1726aaae1aa964cce4fcfa3d03520'? +y +y +y +y +[dshekar@lxplus980 RDF]$ y +-bash: y: command not found +[dshekar@lxplus980 RDF]$ y +-bash: y: command not found +[dshekar@lxplus980 RDF]$ y +-bash: y: command not found +[dshekar@lxplus980 RDF]$ y +-bash: y: command not found +[dshekar@lxplus980 RDF]$ grep -ril "DanushShekar" +^C +[dshekar@lxplus980 RDF]$ ls +Latinos mkShapesRDF mkShapesRDF_postproc PlotsConfigurationsRun3 PlotsConfigurationsRun3_fork +[dshekar@lxplus980 RDF]$ git clone https://github.com/latinos/mkShapesRDF.git mkShapesRDF_privPostProc +Cloning into 'mkShapesRDF_privPostProc'... +remote: Enumerating objects: 5957, done. +remote: Counting objects: 100% (142/142), done. +remote: Compressing objects: 100% (89/89), done. +remote: Total 5957 (delta 93), reused 76 (delta 53), pack-reused 5815 (from 2) +Receiving objects: 100% (5957/5957), 15.62 MiB | 20.86 MiB/s, done. +Resolving deltas: 100% (3939/3939), done. +Updating files: 100% (742/742), done. +[dshekar@lxplus980 RDF]$ exit +logout +Connection to lxplus.cern.ch closed. +danush@danush-5 ZpTreweighting % lxplus +(dshekar@lxplus.cern.ch) Password: +(dshekar@lxplus.cern.ch) Password: +(dshekar@lxplus.cern.ch) Your 2nd factor (dshekar): 762458 +* ******************************************************************** +* Welcome to lxplus938.cern.ch, Red Hat Enterprise Linux release 9.7 (Plow) +* Archive of news is available in /etc/motd-archive +* Reminder: you have agreed to the CERN +* computing rules, in particular OC5. CERN implements +* the measures necessary to ensure compliance. +* https://cern.ch/ComputingRules +* Puppet environment: production, Roger state: production +* Foreman hostgroup: lxplus/nodes/login +* Availability zone: cern-geneva-c +* LXPLUS Public Login Service - http://lxplusdoc.web.cern.ch/ +* Please read LXPLUS Privacy Notice in http://cern.ch/go/TpV7 +* ******************************************************************** +* /!\ LxPlus now requires 2FA from outside of CERN /!\ +* All details for 2FA enforcing on https://cern.ch/otg0156449 + +Last failed login: Tue May 12 15:44:57 CEST 2026 from 192.190.216.122 on ssh:notty +There was 1 failed login attempt since the last successful login. +[dshekar@lxplus938 ~]$ cd public/RDF/mkShapesRDF_privPostProc/ +[dshekar@lxplus938 mkShapesRDF_privPostProc]$ source start.sh +first source of start.sh +(myenv) [dshekar@lxplus938 mkShapesRDF_privPostProc]$ mkPostProc -o 0 -p Summer23_130x_nAODv12_Full2023v12 -s MCl2loose2023v12__MCCorr2023v12JetScaling__l2tight -T ZH_Zto2L_Hto2WtoLNu2Q + xxx +cp: cannot stat 'xxx': No such file or directory +['Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set', 'In order to run please obtain valid proxy via "voms-proxy-init -voms cms -rfc"', 'and setup X509_USER_PROXY or setup X509_USER_KEY/X509_USER_CERT in your environment'] 3 +root://cms-xrd-global.cern.ch/Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set + + +Try with chunksize 3 +Iteration: 0 +Error in : [ERROR] Server responded with an error: [3010] Opening relative path 'Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set' is disallowed. + +Error in : [ERROR] Server responded with an error: [3010] Opening relative path 'Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set' is disallowed. + +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/myenv/bin/mkPostProc", line 7, in + sys.exit(main()) + ~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/scripts/mkPostProc.py", line 225, in main + a.run() + ~~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/framework/processor.py", line 477, in run + genEventSumw += computeSumW(files, len(files)) + ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/framework/processor.py", line 457, in computeSumW + df = ROOT.RDataFrame("Runs", files_tmp) + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/ROOT/_pythonization/_rdf_namespace.py", line 103, in rdataframe + rdf = local_rdf(*args, **kwargs) +TypeError: none of the 10 overloaded methods succeeded. Full details: + RDataFrame::RDataFrame(string_view treename, initializer_list filenames, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + invalid_argument: RDataFrame: could not open file "root://cms-xrd-global.cern.ch/Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set". + RDataFrame::RDataFrame(ROOT::RDataFrame&&) => + TypeError: takes at most 1 arguments (2 given) + RDataFrame::RDataFrame(string_view treeName, TDirectory* dirPtr, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + TypeError: could not convert argument 2 + RDataFrame::RDataFrame(TTree& tree, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + TypeError: could not convert argument 1 + RDataFrame::RDataFrame(const ROOT::RDataFrame&) => + TypeError: takes at most 1 arguments (2 given) + RDataFrame::RDataFrame(string_view treename, const vector& filenames, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + invalid_argument: RDataFrame: could not open file "root://cms-xrd-global.cern.ch/Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set". + bad argument type for built-in operation + RDataFrame::RDataFrame(ULong64_t numEntries) => + TypeError: takes at most 1 arguments (2 given) + RDataFrame::RDataFrame(unique_ptr, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + TypeError: could not convert argument 1 + RDataFrame::RDataFrame(ROOT::RDF::Experimental::RDatasetSpec spec) => + TypeError: takes at most 1 arguments (2 given) +(myenv) [dshekar@lxplus938 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer23_130x_nAODv12.py +(myenv) [dshekar@lxplus938 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/samplesCrossSections_13p6TeV.py +(failed reverse-i-search)`coms': git clone https://github.^Cm/latinos/mkShapesRDF.git mkShapesRDF_privPostProc +(myenv) [dshekar@lxplus938 mkShapesRDF_privPostProc]$ voms-proxy-init --voms cms -valid 192:0 +Enter GRID pass phrase: +Your identity: /DC=ch/DC=cern/OU=Organic Units/OU=Users/CN=dshekar/CN=862901/CN=Danush Shekar +Contacting voms-cms-auth.cern.ch:443 [/DC=ch/DC=cern/OU=computers/CN=cms-auth.cern.ch] "cms"... Done + +Created proxy in /tmp/x509up_u154072. + +Your proxy is valid until Wed May 20 15:51:08 2026 CEST + +(myenv) [dshekar@lxplus938 mkShapesRDF_privPostProc]$ mkPostProc -o 0 -p Summer23_130x_nAODv12_Full2023v12 -s MCl2loose2023v12__MCCorr2023v12JetScaling__l2tight -T ZH_Zto2L_Hto2WtoLNu2Q +/tmp/x509up_u154072 +['Validation error: unmatched dataset pattern'] 1 +root://cms-xrd-global.cern.ch/Validation error: unmatched dataset pattern + + +Try with chunksize 1 +Iteration: 0 +Error in : [ERROR] Server responded with an error: [3010] Opening relative path 'Validation error: unmatched dataset pattern' is disallowed. + +Error in : [ERROR] Server responded with an error: [3010] Opening relative path 'Validation error: unmatched dataset pattern' is disallowed. + +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/myenv/bin/mkPostProc", line 7, in + sys.exit(main()) + ~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/scripts/mkPostProc.py", line 225, in main + a.run() + ~~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/framework/processor.py", line 477, in run + genEventSumw += computeSumW(files, len(files)) + ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/framework/processor.py", line 457, in computeSumW + df = ROOT.RDataFrame("Runs", files_tmp) + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/ROOT/_pythonization/_rdf_namespace.py", line 103, in rdataframe + rdf = local_rdf(*args, **kwargs) +TypeError: none of the 10 overloaded methods succeeded. Full details: + RDataFrame::RDataFrame(string_view treename, initializer_list filenames, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + invalid_argument: RDataFrame: could not open file "root://cms-xrd-global.cern.ch/Validation error: unmatched dataset pattern". + RDataFrame::RDataFrame(ROOT::RDataFrame&&) => + TypeError: takes at most 1 arguments (2 given) + RDataFrame::RDataFrame(string_view treeName, TDirectory* dirPtr, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + TypeError: could not convert argument 2 + RDataFrame::RDataFrame(TTree& tree, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + TypeError: could not convert argument 1 + RDataFrame::RDataFrame(const ROOT::RDataFrame&) => + TypeError: takes at most 1 arguments (2 given) + RDataFrame::RDataFrame(string_view treename, const vector& filenames, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + invalid_argument: RDataFrame: could not open file "root://cms-xrd-global.cern.ch/Validation error: unmatched dataset pattern". + bad argument type for built-in operation + RDataFrame::RDataFrame(ULong64_t numEntries) => + TypeError: takes at most 1 arguments (2 given) + RDataFrame::RDataFrame(unique_ptr, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + TypeError: could not convert argument 1 + RDataFrame::RDataFrame(ROOT::RDF::Experimental::RDatasetSpec spec) => + TypeError: takes at most 1 arguments (2 given) +(myenv) [dshekar@lxplus938 mkShapesRDF_privPostProc]$ Read from remote host lxplus.cern.ch: Operation timed out +Connection to lxplus.cern.ch closed. +client_loop: send disconnect: Broken pipe +danush@danush-5 ZpTreweighting % lxplus +(dshekar@lxplus.cern.ch) Password: +(dshekar@lxplus.cern.ch) Your 2nd factor (dshekar): 078732 +* ******************************************************************** +* Welcome to lxplus927.cern.ch, Red Hat Enterprise Linux release 9.7 (Plow) +* Archive of news is available in /etc/motd-archive +* Reminder: you have agreed to the CERN +* computing rules, in particular OC5. CERN implements +* the measures necessary to ensure compliance. +* https://cern.ch/ComputingRules +* Puppet environment: production, Roger state: production +* Foreman hostgroup: lxplus/nodes/login +* Availability zone: cern-geneva-b +* LXPLUS Public Login Service - http://lxplusdoc.web.cern.ch/ +* Please read LXPLUS Privacy Notice in http://cern.ch/go/TpV7 +* ******************************************************************** +* /!\ LxPlus now requires 2FA from outside of CERN /!\ +* All details for 2FA enforcing on https://cern.ch/otg0156449 + +[dshekar@lxplus927 ~]$ cd public/RDF/mkShapesRDF_privPostProc/ +[dshekar@lxplus927 mkShapesRDF_privPostProc]$ source start.sh +first source of start.sh +vim (myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer23_130x_nAODv12.py +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ mkPostProc -o 0 -p Summer23_130x_nAODv12_Full2023v12 -s MCl2loose2023v12__MCCorr2023v12JetScaling__l2tight -T ZH_Zto2L_Hto2WtoLNu2Q + xxx +cp: cannot stat 'xxx': No such file or directory +['Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set', 'In order to run please obtain valid proxy via "voms-proxy-init -voms cms -rfc"', 'and setup X509_USER_PROXY or setup X509_USER_KEY/X509_USER_CERT in your environment'] 3 +root://cms-xrd-global.cern.ch/Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set +^C^C^C^C^CTraceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/myenv/bin/mkPostProc", line 7, in + sys.exit(main()) + ~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/scripts/mkPostProc.py", line 225, in main + a.run() + ~~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/framework/processor.py", line 437, in run + import ROOT + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/ROOT/__init__.py", line 20, in + import cppyy + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/cppyy/__init__.py", line 74, in + from ._cpython_cppyy import * + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/cppyy/_cpython_cppyy.py", line 35, in + import cppyy.libcppyy as _backend +KeyboardInterrupt + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ voms-proxy-init --voms cms -valid 192:0 +Enter GRID pass phrase: +Your identity: /DC=ch/DC=cern/OU=Organic Units/OU=Users/CN=dshekar/CN=862901/CN=Danush Shekar +Contacting voms-cms-auth.cern.ch:443 [/DC=ch/DC=cern/OU=computers/CN=cms-auth.cern.ch] "cms"... Done + +Created proxy in /tmp/x509up_u154072. + +Your proxy is valid until Wed May 20 17:09:01 2026 CEST + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ mkPostProc -o 0 -p Summer23_130x_nAODv12_Full2023v12 -s MCl2loose2023v12__MCCorr2023v12JetScaling__l2tight -T ZH_Zto2L_Hto2WtoLNu2Q +/tmp/x509up_u154072 +['/store/mc/Run3Summer23NanoAODv12/ZH_ZtoAll_Hto2Wto2L2Nu_M-125_TuneCP5_13p6TeV_powheg-minlo-HZJ-jhugenv752-pythia8/NANOAODSIM/130X_mcRun3_2023_realistic_v15-v3/50000/f9a9e3b2-2d1a-4de3-9f73-7eabe863dc03.root', '/store/mc/Run3Summer23NanoAODv12/ZH_ZtoAll_Hto2Wto2L2Nu_M-125_TuneCP5_13p6TeV_powheg-minlo-HZJ-jhugenv752-pythia8/NANOAODSIM/130X_mcRun3_2023_realistic_v15-v3/50000/4cb8bd44-4515-4336-b108-94e00f3daceb.root', '/store/mc/Run3Summer23NanoAODv12/ZH_ZtoAll_Hto2Wto2L2Nu_M-125_TuneCP5_13p6TeV_powheg-minlo-HZJ-jhugenv752-pythia8/NANOAODSIM/130X_mcRun3_2023_realistic_v15-v3/50000/10022c89-b6b2-4b88-af38-385f3029bbcd.root', 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'/store/mc/Run3Summer23NanoAODv12/ZH_ZtoAll_Hto2Wto2L2Nu_M-125_TuneCP5_13p6TeV_powheg-minlo-HZJ-jhugenv752-pythia8/NANOAODSIM/130X_mcRun3_2023_realistic_v15-v3/40000/6cd2a7fc-dc85-44b0-9cb7-da02448377d6.root'] 72 +root://cms-xrd-global.cern.ch//store/mc/Run3Summer23NanoAODv12/ZH_ZtoAll_Hto2Wto2L2Nu_M-125_TuneCP5_13p6TeV_powheg-minlo-HZJ-jhugenv752-pythia8/NANOAODSIM/130X_mcRun3_2023_realistic_v15-v3/50000/f9a9e3b2-2d1a-4de3-9f73-7eabe863dc03.root + + +Try with chunksize 72 +Iteration: 0 +Warning in : no dictionary for class edm::Hash<1> is available +Warning in : no dictionary for class edm::ProcessHistory is available +Warning in : no dictionary for class edm::ProcessConfiguration is available +Warning in : no dictionary for class edm::ParameterSetBlob is available +Warning in : no dictionary for class pair,edm::ParameterSetBlob> is available +Finally computed sumW: 2096126.3476530013 +Submitting job(s)........................................................................ +72 job(s) submitted to cluster 15584857. +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer23_130x_nAODv12.py +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/12/26 17:17:59 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS +dshekar ID: 15584857 5/12 17:09 _ 72 _ 72 15584857.0-71 + +Total for query: 72 jobs; 0 completed, 0 removed, 0 idle, 72 running, 0 held, 0 suspended +Total for dshekar: 72 jobs; 0 completed, 0 removed, 0 idle, 72 running, 0 held, 0 suspended +Total for all users: 18410 jobs; 6692 completed, 493 removed, 1185 idle, 10029 running, 11 held, 0 suspended + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/12/26 17:24:57 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS +dshekar ID: 15584857 5/12 17:09 _ 61 11 72 15584857.0-71 + +Total for query: 72 jobs; 0 completed, 0 removed, 11 idle, 61 running, 0 held, 0 suspended +Total for dshekar: 72 jobs; 0 completed, 0 removed, 11 idle, 61 running, 0 held, 0 suspended +Total for all users: 18401 jobs; 6700 completed, 493 removed, 1305 idle, 9886 running, 17 held, 0 suspended + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/12/26 17:47:55 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS +dshekar ID: 15584857 5/12 17:09 57 15 _ 72 15584857.0-71 + +Total for query: 15 jobs; 0 completed, 0 removed, 0 idle, 15 running, 0 held, 0 suspended +Total for dshekar: 15 jobs; 0 completed, 0 removed, 0 idle, 15 running, 0 held, 0 suspended +Total for all users: 18232 jobs; 6702 completed, 493 removed, 1736 idle, 9267 running, 34 held, 0 suspended + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/12/26 18:42:53 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS +dshekar ID: 15584857 5/12 17:09 68 4 _ 72 15584857.2-68 + +Total for query: 4 jobs; 0 completed, 0 removed, 0 idle, 4 running, 0 held, 0 suspended +Total for dshekar: 4 jobs; 0 completed, 0 removed, 0 idle, 4 running, 0 held, 0 suspended +Total for all users: 18073 jobs; 6704 completed, 493 removed, 902 idle, 9970 running, 4 held, 0 suspended + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/samplesCrossSections_13p6TeV.py +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/12/26 18:44:48 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS +dshekar ID: 15584857 5/12 17:09 68 4 _ 72 15584857.2-68 + +Total for query: 4 jobs; 0 completed, 0 removed, 0 idle, 4 running, 0 held, 0 suspended +Total for dshekar: 4 jobs; 0 completed, 0 removed, 0 idle, 4 running, 0 held, 0 suspended +Total for all users: 18069 jobs; 6704 completed, 493 removed, 941 idle, 9895 running, 36 held, 0 suspended + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ ls +Dockerfile docs environment.yml examples install.sh MANIFEST.in mkShapesRDF mkShapesRDF.egg-info myenv pyproject.toml README.md setup.cfg start.sh tests utils +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ cp /cvmfs/cms.cern.ch/phys_generator/gridpacks/PdmV/Run3Summer22/Powheg/WZ/WZ_slc7_amd64_gcc10_CMSSW_12_4_8_WZto3LNu_powheg-pythia8.tgz ./ +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ unzip WZ_slc7_amd64_gcc10_CMSSW_12_4_8_WZto3LNu_powheg^C +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ ls +Dockerfile docs environment.yml examples install.sh MANIFEST.in mkShapesRDF mkShapesRDF.egg-info myenv pyproject.toml README.md setup.cfg start.sh tests utils WZ_slc7_amd64_gcc10_CMSSW_12_4_8_WZto3LNu_powheg-pythia8.tgz +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ tar -xzvf WZ_slc7_amd64_gcc10_CMSSW_12_4_8_WZto3LNu_powheg-pythia8.tgz +FlavRegList +VERSION +bornequiv +mint_upb_btildeupb.top +mint_upb_remnupb.top +powheg.input +pwg-btlgrid.top +pwg-rmngrid.top +pwg-rwl.dat +pwg-stat.dat +pwgborngrid.top +pwgboundviolations.dat +pwgbtildeupb.dat +pwgcounters.dat +pwgfullgrid.dat +pwggrid.dat +pwghistnorms.top +pwgremnupb.dat +pwgubound.dat +pwgxgrid.dat +pwhg_checklimits +pwhg_main +realequivregions-btl +realequivregions-rad +runcmsgrid.sh +runcmsgrid_par.sh +virtequiv +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ ls +bornequiv examples mint_upb_btildeupb.top myenv pwgbtildeupb.dat pwggrid.dat pwg-rwl.dat pwhg_checklimits realequivregions-btl setup.cfg VERSION +Dockerfile FlavRegList mint_upb_remnupb.top powheg.input pwg-btlgrid.top pwghistnorms.top pwg-stat.dat pwhg_main realequivregions-rad start.sh virtequiv +docs install.sh mkShapesRDF pwgborngrid.top pwgcounters.dat pwgremnupb.dat pwgubound.dat pyproject.toml runcmsgrid_par.sh tests WZ_slc7_amd64_gcc10_CMSSW_12_4_8_WZto3LNu_powheg-pythia8.tgz +environment.yml MANIFEST.in mkShapesRDF.egg-info pwgboundviolations.dat pwgfullgrid.dat pwg-rmngrid.top pwgxgrid.dat README.md runcmsgrid.sh utils +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm FlavRegList VERSION bornequiv mint_upb_ +mint_upb_btildeupb.top mint_upb_remnupb.top +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm FlavRegList VERSION bornequiv mint_upb_* +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm pwg +pwgborngrid.top pwgbtildeupb.dat pwgcounters.dat pwggrid.dat pwgremnupb.dat pwg-rwl.dat pwgubound.dat +pwgboundviolations.dat pwg-btlgrid.top pwgfullgrid.dat pwghistnorms.top pwg-rmngrid.top pwg-stat.dat pwgxgrid.dat +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm pwg* +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm pwhg_ +pwhg_checklimits pwhg_main +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm pwhg_ +pwhg_checklimits pwhg_main +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm pwhg_* +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm realequivregions-* +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm runcmsgrid +runcmsgrid_par.sh runcmsgrid.sh +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm runcmsgrid* +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ rm virtequiv +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ ls +Dockerfile docs environment.yml examples install.sh MANIFEST.in mkShapesRDF mkShapesRDF.egg-info myenv powheg.input pyproject.toml README.md setup.cfg start.sh tests utils WZ_slc7_amd64_gcc10_CMSSW_12_4_8_WZto3LNu_powheg-pythia8.tgz +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim powheg.input +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/12/26 19:14:47 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS +dshekar ID: 15584857 5/12 17:09 68 4 _ 72 15584857.2-68 + +Total for query: 4 jobs; 0 completed, 0 removed, 0 idle, 4 running, 0 held, 0 suspended +Total for dshekar: 4 jobs; 0 completed, 0 removed, 0 idle, 4 running, 0 held, 0 suspended +Total for all users: 17845 jobs; 6704 completed, 493 removed, 754 idle, 9858 running, 36 held, 0 suspended + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/12/26 19:57:09 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS +dshekar ID: 15584857 5/12 17:09 69 3 _ 72 15584857.2-40 + +Total for query: 3 jobs; 0 completed, 0 removed, 0 idle, 3 running, 0 held, 0 suspended +Total for dshekar: 3 jobs; 0 completed, 0 removed, 0 idle, 3 running, 0 held, 0 suspended +Total for all users: 17918 jobs; 6705 completed, 493 removed, 768 idle, 9916 running, 36 held, 0 suspended + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_rm all + +Couldn't find/remove all jobs of user "all" +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_rm -all +All jobs have been marked for removal +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/12/26 19:57:50 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE HOLD TOTAL JOB_IDS + +Total for query: 0 jobs; 0 completed, 0 removed, 0 idle, 0 running, 0 held, 0 suspended +Total for dshekar: 0 jobs; 0 completed, 0 removed, 0 idle, 0 running, 0 held, 0 suspended +Total for all users: 17922 jobs; 6705 completed, 493 removed, 775 idle, 9913 running, 36 held, 0 suspended + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/12/26 20:41:11 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE HOLD TOTAL JOB_IDS + +Total for query: 0 jobs; 0 completed, 0 removed, 0 idle, 0 running, 0 held, 0 suspended +Total for dshekar: 0 jobs; 0 completed, 0 removed, 0 idle, 0 running, 0 held, 0 suspended +Total for all users: 17155 jobs; 6706 completed, 493 removed, 685 idle, 9229 running, 42 held, 0 suspended + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer23_130x_nAODv12.py +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer22_130x_nAODv12.py +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer23_130x_nAODv12.py +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer22_130x_nAODv12.py +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ mkPostProc -o 0 -p Summer22_130x_nAODv12_Full2022v12 -s MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight -T GluGluZH_Zto2L_Hto2WtoLNu2Q +/tmp/x509up_u154072 +['/store/mc/Run3Summer22NanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/93766030-c43d-4b88-bfc4-868d4240fa5a.root', 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'/store/mc/Run3Summer22NanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/f99e2778-6481-4df3-ab4d-5e7ce8dc7d98.root', '/store/mc/Run3Summer22NanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/3ef9dca2-9017-481a-8223-293084a352f6.root', '/store/mc/Run3Summer22NanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/dddce4d4-cea8-4ded-898a-cb6915a46ed8.root', '/store/mc/Run3Summer22NanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/a9919cda-a903-4bf5-8ff8-ba1f3884a4e6.root', 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'/store/mc/Run3Summer22NanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/3830afb7-368a-4228-a06d-4b66638f1573.root', '/store/mc/Run3Summer22NanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/8ec671d7-4117-4837-ac88-0e278f838668.root', '/store/mc/Run3Summer22NanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/9d0f33f4-5b6f-49bb-b527-1467e50874be.root', '/store/mc/Run3Summer22NanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/5034c532-7182-4466-9ca0-95ff6efe254e.root'] 25 +root://cms-xrd-global.cern.ch//store/mc/Run3Summer22NanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v2/2550000/93766030-c43d-4b88-bfc4-868d4240fa5a.root + + +Try with chunksize 25 +Iteration: 0 +Warning in : no dictionary for class edm::Hash<1> is available +Warning in : no dictionary for class edm::ProcessHistory is available +Warning in : no dictionary for class edm::ProcessConfiguration is available +Warning in : no dictionary for class edm::ParameterSetBlob is available +Warning in : no dictionary for class pair,edm::ParameterSetBlob> is available +Finally computed sumW: 33861.7034 +Submitting job(s)......................... +25 job(s) submitted to cluster 15586739. +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer22 +Summer22_130x_nAODv12.py Summer22EE_130x_nAODv12.py Summer22EE_130x_nAODv12_testrecipes.py +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer22EE_130x_nAODv12 +Summer22EE_130x_nAODv12.py Summer22EE_130x_nAODv12_testrecipes.py +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer22EE_130x_nAODv12.py +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ mkPostProc -o 0 -p Summer22EE_130x_nAODv12_Full2022v12 -s MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight -T ZH_Zto2L_Hto2WtoLNu2Q +/tmp/x509up_u154072 +['/store/mc/Run3Summer22EENanoAODv12/ZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-minlo-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/100000/3956a7a1-4651-4676-afd2-3b612d07adf1.root', '/store/mc/Run3Summer22EENanoAODv12/ZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-minlo-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/100000/13ea09c9-a1d8-4878-afdc-556722648d03.root', 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20:57:52 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS +dshekar ID: 15586739 5/12 20:52 _ 25 _ 25 15586739.0-24 +dshekar ID: 15586740 5/12 20:55 _ _ 46 46 15586740.0-45 + +Total for query: 71 jobs; 0 completed, 0 removed, 46 idle, 25 running, 0 held, 0 suspended +Total for dshekar: 71 jobs; 0 completed, 0 removed, 46 idle, 25 running, 0 held, 0 suspended +Total for all users: 16578 jobs; 6706 completed, 493 removed, 746 idle, 8569 running, 64 held, 0 suspended + +(myenv) [dshekar@lxplus927 mkShapesRDF_privPostProc]$ exit +logout +Connection to lxplus.cern.ch closed. +danush@danush-5 ZpTreweighting % lxplus +(dshekar@lxplus.cern.ch) Password: +(dshekar@lxplus.cern.ch) Your 2nd factor (dshekar): 269284 +* ******************************************************************** +* Welcome to lxplus956.cern.ch, Red Hat Enterprise Linux release 9.7 (Plow) +* Archive of news is available in /etc/motd-archive +* Reminder: you have agreed to the CERN +* computing rules, in particular OC5. CERN implements +* the measures necessary to ensure compliance. +* https://cern.ch/ComputingRules +* Puppet environment: production, Roger state: production +* Foreman hostgroup: lxplus/nodes/login +* Availability zone: cern-geneva-c +* LXPLUS Public Login Service - http://lxplusdoc.web.cern.ch/ +* Please read LXPLUS Privacy Notice in http://cern.ch/go/TpV7 +* ******************************************************************** +* /!\ LxPlus now requires 2FA from outside of CERN /!\ +* All details for 2FA enforcing on https://cern.ch/otg0156449 + +[dshekar@lxplus956 ~]$ cd /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/^C +[dshekar@lxplus956 ~]$ pwd +/afs/cern.ch/user/d/dshekar +[dshekar@lxplus956 ~]$ cd /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/ +[dshekar@lxplus956 HWWNano]$ ls +Autumn18_102X_nAODv7_Full2018v7 Run2022_ReReco_nAODv12_Full2022v12_OLD Summer16_102X_nAODv7_Full2016v7 +Embedding2016_102X_nAODv7_Full2016v7 Run2023BPix_Prompt_nAODv12_Full2023BPixv12 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(y or n) +[dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ cp nanoLatino_WZ +nanoLatino_WZ__part0.root nanoLatino_WZ__part26.root nanoLatino_WZ__part42.root nanoLatino_WZTo3LNu__part18.root nanoLatino_WZZ__part11.root +nanoLatino_WZ__part10.root nanoLatino_WZ__part27.root nanoLatino_WZ__part43.root nanoLatino_WZTo3LNu__part19.root nanoLatino_WZZ__part12.root +nanoLatino_WZ__part11.root nanoLatino_WZ__part28.root nanoLatino_WZ__part44.root nanoLatino_WZTo3LNu__part1.root nanoLatino_WZZ__part13.root +nanoLatino_WZ__part12.root nanoLatino_WZ__part29.root nanoLatino_WZ__part4.root nanoLatino_WZTo3LNu__part20.root nanoLatino_WZZ__part14.root +nanoLatino_WZ__part13.root nanoLatino_WZ__part2.root nanoLatino_WZ__part5.root nanoLatino_WZTo3LNu__part21.root nanoLatino_WZZ__part15.root +nanoLatino_WZ__part14.root nanoLatino_WZ__part30.root nanoLatino_WZ__part6.root nanoLatino_WZTo3LNu__part22.root nanoLatino_WZZ__part16.root +nanoLatino_WZ__part15.root nanoLatino_WZ__part31.root nanoLatino_WZ__part7.root nanoLatino_WZTo3LNu__part23.root nanoLatino_WZZ__part17.root +nanoLatino_WZ__part16.root nanoLatino_WZ__part32.root nanoLatino_WZ__part8.root nanoLatino_WZTo3LNu__part24.root nanoLatino_WZZ__part18.root +nanoLatino_WZ__part17.root nanoLatino_WZ__part33.root nanoLatino_WZ__part9.root nanoLatino_WZTo3LNu__part2.root nanoLatino_WZZ__part1.root +nanoLatino_WZ__part18.root nanoLatino_WZ__part34.root nanoLatino_WZTo3LNu__part0.root nanoLatino_WZTo3LNu__part3.root nanoLatino_WZZ__part2.root +nanoLatino_WZ__part19.root nanoLatino_WZ__part35.root nanoLatino_WZTo3LNu__part10.root nanoLatino_WZTo3LNu__part4.root nanoLatino_WZZ__part3.root +nanoLatino_WZ__part1.root nanoLatino_WZ__part36.root nanoLatino_WZTo3LNu__part11.root nanoLatino_WZTo3LNu__part5.root nanoLatino_WZZ__part4.root +nanoLatino_WZ__part20.root nanoLatino_WZ__part37.root nanoLatino_WZTo3LNu__part12.root nanoLatino_WZTo3LNu__part6.root nanoLatino_WZZ__part5.root +nanoLatino_WZ__part21.root nanoLatino_WZ__part38.root nanoLatino_WZTo3LNu__part13.root nanoLatino_WZTo3LNu__part7.root nanoLatino_WZZ__part6.root +nanoLatino_WZ__part22.root nanoLatino_WZ__part39.root nanoLatino_WZTo3LNu__part14.root nanoLatino_WZTo3LNu__part8.root nanoLatino_WZZ__part7.root +nanoLatino_WZ__part23.root nanoLatino_WZ__part3.root nanoLatino_WZTo3LNu__part15.root nanoLatino_WZTo3LNu__part9.root nanoLatino_WZZ__part8.root +nanoLatino_WZ__part24.root nanoLatino_WZ__part40.root nanoLatino_WZTo3LNu__part16.root nanoLatino_WZZ__part0.root nanoLatino_WZZ__part9.root +nanoLatino_WZ__part25.root nanoLatino_WZ__part41.root nanoLatino_WZTo3LNu__part17.root nanoLatino_WZZ__part10.root +[dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ cp nanoLatino_WZ* /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22 +Summer22_130x_nAODv12_Full2022v12/ Summer22EE_130x_nAODv12_Full2022v12/ +[dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ cp nanoLatino_WZ* /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/^C +[dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ pwd +/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight +[dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ cp nanoLatino_WZ* /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_FlavorQCDup_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jerdo_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_HF_2022do_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jerup_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_HF_2022up_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_Absolute_2022do_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_HFdo_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_Absolute_2022up_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_HFup_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_Absolutedo_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_RelativeBaldo_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_Absoluteup_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_RelativeBalup_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_BBEC1_2022do_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_RelativeSample_2022do_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_BBEC1_2022up_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_RelativeSample_2022up_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_BBEC1do_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__leptonResolutiondo_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_BBEC1up_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__leptonResolutionup_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_EC2_2022do_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__leptonScaledo_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_EC2_2022up_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__leptonScaleup_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_EC2do_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__unclustEndo_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_EC2up_suffix/ MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__unclustEnup_suffix/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_FlavorQCDdo_suffix/ +[dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ cp nanoLatino_WZ* /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +[dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ cd +[dshekar@lxplus956 ~]$ cd public/RDF/ +Latinos/ mkShapesRDF/ mkShapesRDF_postproc/ mkShapesRDF_privPostProc/ PlotsConfigurationsRun3/ PlotsConfigurationsRun3_fork/ +[dshekar@lxplus956 ~]$ cd public/RDF/PlotsConfigurationsRun3/ +ControlRegions/ FakeRate/ HWW/ LeptonID/ RPV/ VBF_differential/ WH_chargeAsymmetry/ WW_Run3/ ZpTreweighting/ +examples/ .git/ HWW_polarization/ __MACOSX/ utils/ VBS_OS_pol/ WH_SS/ ZH3l_BDT/ +[dshekar@lxplus956 ~]$ cd public/RDF/PlotsConfigurationsRun3/Z +ZH3l_BDT/ ZpTreweighting/ +[dshekar@lxplus956 ~]$ cd public/RDF/PlotsConfigurationsRun3/Z +ZH3l_BDT/ ZpTreweighting/ +[dshekar@lxplus956 ~]$ cd public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/ +[dshekar@lxplus956 ZH3l_BDT]$ ls +bdt_wh_chargeAsymmetry macros run2.tar.gz zh3l_run2_bdt_rdf zh3l_run2_rdf +[dshekar@lxplus956 ZH3l_BDT]$ rm run2.tar.gz +[dshekar@lxplus956 ZH3l_BDT]$ pwd +/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT +[dshekar@lxplus956 ZH3l_BDT]$ rdfenv +first source of start.sh +(myenv) [dshekar@lxplus956 ZH3l_BDT]$ ls +bdt_wh_chargeAsymmetry macros zh3l_run2_bdt_rdf zh3l_run2_rdf zh3l_run3_bdt.tar.gz +(myenv) [dshekar@lxplus956 ZH3l_BDT]$ ls +bdt_wh_chargeAsymmetry macros zh3l_run2_bdt_rdf zh3l_run2_rdf zh3l_run3_bdt.tar.gz +(myenv) [dshekar@lxplus956 ZH3l_BDT]$ tar -xzvf zh3l_run3_bdt.tar.gz +._zh3l_run3_bdt +tar: Ignoring unknown extended header keyword 'LIBARCHIVE.xattr.com.apple.provenance' +zh3l_run3_bdt/ +zh3l_run3_bdt/._bdt_train +tar: Ignoring unknown extended header keyword 'LIBARCHIVE.xattr.com.apple.provenance' +zh3l_run3_bdt/bdt_train/ +zh3l_run3_bdt/bdt_train/._preselections.py +tar: Ignoring unknown extended header keyword 'LIBARCHIVE.xattr.com.apple.provenance' +zh3l_run3_bdt/bdt_train/preselections.py +zh3l_run3_bdt/bdt_train/._cuts_BDT.py +tar: Ignoring unknown extended header keyword 'LIBARCHIVE.xattr.com.apple.provenance' +zh3l_run3_bdt/bdt_train/cuts_BDT.py +zh3l_run3_bdt/bdt_train/._config_BDT.py +tar: Ignoring unknown extended header keyword 'LIBARCHIVE.xattr.com.apple.provenance' +zh3l_run3_bdt/bdt_train/config_BDT.py +zh3l_run3_bdt/bdt_train/._configuration_BDT.py +tar: Ignoring unknown extended header keyword 'LIBARCHIVE.xattr.com.apple.provenance' +zh3l_run3_bdt/bdt_train/configuration_BDT.py +zh3l_run3_bdt/bdt_train/._samples_BDT.py +tar: Ignoring unknown extended header keyword 'LIBARCHIVE.xattr.com.apple.provenance' +zh3l_run3_bdt/bdt_train/samples_BDT.py +zh3l_run3_bdt/bdt_train/._structure.py +tar: Ignoring unknown extended header keyword 'LIBARCHIVE.xattr.com.apple.provenance' +zh3l_run3_bdt/bdt_train/structure.py +zh3l_run3_bdt/bdt_train/._Classification_BDT.py +tar: Ignoring unknown extended header keyword 'LIBARCHIVE.xattr.com.apple.provenance' +zh3l_run3_bdt/bdt_train/Classification_BDT.py +(myenv) [dshekar@lxplus956 ZH3l_BDT]$ ls -ltr +total 17 +drwxr-xr-x. 2 dshekar zh 2048 Nov 3 2025 bdt_wh_chargeAsymmetry +drwxr-xr-x. 2 dshekar zh 2048 Nov 3 2025 macros +drwxr-xr-x. 5 dshekar zh 2048 Dec 17 21:25 zh3l_run2_rdf +drwxr-xr-x. 4 dshekar zh 2048 Dec 18 17:15 zh3l_run2_bdt_rdf +drwxr-xr-x. 3 dshekar zh 2048 May 13 04:29 zh3l_run3_bdt +-rw-r--r--. 1 dshekar zh 6736 May 13 04:49 zh3l_run3_bdt.tar.gz +(myenv) [dshekar@lxplus956 ZH3l_BDT]$ rm zh3l_run3_bdt.tar.gz +(myenv) [dshekar@lxplus956 ZH3l_BDT]$ cd zh3l_run3_bdt/ +(myenv) [dshekar@lxplus956 zh3l_run3_bdt]$ ls +bdt_train +(myenv) [dshekar@lxplus956 zh3l_run3_bdt]$ cd bdt_train/ +(myenv) [dshekar@lxplus956 bdt_train]$ ls +Classification_BDT.py config_BDT.py configuration_BDT.py cuts_BDT.py preselections.py samples_BDT.py structure.py +(myenv) [dshekar@lxplus956 bdt_train]$ python3 Classification_BDT.py +Need to query for files for folder /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 8, in + import config_BDT as config + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/config_BDT.py", line 21, in + exec(handle.read()) + ~~~~^^^^^^^^^^^^^^^ + File "", line 217, in + File "", line 212, in makeMCDirectory +NameError: name 'mcProduction' is not defined +(myenv) [dshekar@lxplus956 bdt_train]$ vim samples_BDT.py +(myenv) [dshekar@lxplus956 bdt_train]$ vim samples_BDT.py +(myenv) [dshekar@lxplus956 bdt_train]$ python3 Classification_BDT.py +Need to query for files for folder /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 8, in + import config_BDT as config + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/config_BDT.py", line 21, in + exec(handle.read()) + ~~~~^^^^^^^^^^^^^^^ + File "", line 218, in + File "", line 213, in makeMCDirectory +NameError: name 'mcSteps' is not defined +(myenv) [dshekar@lxplus956 bdt_train]$ python3 Classification_BDT.py +^CTraceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 2, in + from ROOT import TMVA, TFile, TTree, TCut, TChain + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/ROOT/__init__.py", line 52, in + _register_pythonizations() + ~~~~~~~~~~~~~~~~~~~~~~~~^^ + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/ROOT/_pythonization/__init__.py", line 337, in _register_pythonizations + importlib.import_module(__name__ + "." + module_name) + ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/python3.13/importlib/__init__.py", line 88, in import_module + return _bootstrap._gcd_import(name[level:], package, level) + ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/ROOT/_pythonization/_tclass.py", line 25, in + pythonize_tclass() + ~~~~~~~~~~~~~~~~^^ + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/ROOT/_pythonization/_tclass.py", line 16, in pythonize_tclass + klass = cppyy.gbl.TClass + ^^^^^^^^^^^^^^^^ +AttributeError: has no attribute 'TClass'. Full details: + type object '' has no attribute 'TClass' + + 'TClass' is not a known C++ template + 'TClass' is not a known C++ enum +(myenv) [dshekar@lxplus956 bdt_train]$ vim samples_BDT.py +(myenv) [dshekar@lxplus956 bdt_train]$ python3 Classification_BDT.py +Need to query for files for folder /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +MuonEG_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Muon_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +EGamma_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +MuonEG_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Muon_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +EGamma_Run2022D-ReReco-v1 +Input arguments: ['Classification_BDT.py'] +No suffix, running with standard output name +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 82, in + runJob() + ~~~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 13, in runJob + TMVA.PyMethodBase.PyInitialize() + ^^^^^^^^^^^^^^^^^ +AttributeError: has no attribute 'PyMethodBase'. Full details: + type object 'TMVA' has no attribute 'PyMethodBase' + 'TMVA::PyMethodBase' is not a known C++ class + 'PyMethodBase' is not a known C++ template + 'PyMethodBase' is not a known C++ enum +(myenv) [dshekar@lxplus956 bdt_train]$ python Classification_BDT.py +Need to query for files for folder /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +MuonEG_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Muon_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +EGamma_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +MuonEG_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Muon_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +EGamma_Run2022D-ReReco-v1 +Input arguments: ['Classification_BDT.py'] +No suffix, running with standard output name +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 82, in + runJob() + ~~~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 13, in runJob + TMVA.PyMethodBase.PyInitialize() + ^^^^^^^^^^^^^^^^^ +AttributeError: has no attribute 'PyMethodBase'. Full details: + type object 'TMVA' has no attribute 'PyMethodBase' + 'TMVA::PyMethodBase' is not a known C++ class + 'PyMethodBase' is not a known C++ template + 'PyMethodBase' is not a known C++ enum +(myenv) [dshekar@lxplus956 bdt_train]$ vim Classification_BDT.py +(myenv) [dshekar@lxplus956 bdt_train]$ python Classification_BDT.py +Need to query for files for folder /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +MuonEG_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Muon_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +EGamma_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +MuonEG_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Muon_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +EGamma_Run2022D-ReReco-v1 +Input arguments: ['Classification_BDT.py'] +No suffix, running with standard output name +Sample name: WZ +Sub-sample: WZTo3LNu_mllmin01 +Sub-sample: WZTo2L2Q +DataSetInfo : [dataset] : Added class "Background" + : Add Tree Events of type Background with 0 events + : Encountered empty TTree or TChain of class Background +***> abort program execution +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 82, in + runJob() + ~~~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 42, in runJob + dataloader.AddBackgroundTree(sample['tree'], 1.0) + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^ +cppyy.gbl.std.runtime_error: void TMVA::DataLoader::AddBackgroundTree(TTree* background, Double_t weight = 1., TMVA::Types::ETreeType treetype = Types::kMaxTreeType) => + runtime_error: FATAL error +(myenv) [dshekar@lxplus956 bdt_train]$ cd /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/ +(myenv) [dshekar@lxplus956 HWWNano]$ cd Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +(myenv) [dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ ls nanoLatino_ZZ +nanoLatino_ZZ__part0.root nanoLatino_ZZ__part17.root nanoLatino_ZZ__part5.root nanoLatino_ZZZ__part12.root nanoLatino_ZZZ__part1.root nanoLatino_ZZZ__part6.root +nanoLatino_ZZ__part10.root nanoLatino_ZZ__part18.root nanoLatino_ZZ__part6.root nanoLatino_ZZZ__part13.root nanoLatino_ZZZ__part20.root nanoLatino_ZZZ__part7.root +nanoLatino_ZZ__part11.root nanoLatino_ZZ__part19.root nanoLatino_ZZ__part7.root nanoLatino_ZZZ__part14.root nanoLatino_ZZZ__part21.root nanoLatino_ZZZ__part8.root +nanoLatino_ZZ__part12.root nanoLatino_ZZ__part1.root nanoLatino_ZZ__part8.root nanoLatino_ZZZ__part15.root nanoLatino_ZZZ__part22.root nanoLatino_ZZZ__part9.root +nanoLatino_ZZ__part13.root nanoLatino_ZZ__part20.root nanoLatino_ZZ__part9.root nanoLatino_ZZZ__part16.root nanoLatino_ZZZ__part2.root +nanoLatino_ZZ__part14.root nanoLatino_ZZ__part2.root nanoLatino_ZZZ__part0.root nanoLatino_ZZZ__part17.root nanoLatino_ZZZ__part3.root +nanoLatino_ZZ__part15.root nanoLatino_ZZ__part3.root nanoLatino_ZZZ__part10.root nanoLatino_ZZZ__part18.root nanoLatino_ZZZ__part4.root +nanoLatino_ZZ__part16.root nanoLatino_ZZ__part4.root nanoLatino_ZZZ__part11.root nanoLatino_ZZZ__part19.root nanoLatino_ZZZ__part5.root +(myenv) [dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ ^C nanoLatino_ZZ +(myenv) [dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ cp nanoLatino_ZZ* /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +pwd +(myenv) [dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ pwd +/eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight +(myenv) [dshekar@lxplus956 MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight]$ cd +(myenv) [dshekar@lxplus956 ~]$ cd public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/ +(myenv) [dshekar@lxplus956 zh3l_run3_bdt]$ cd bdt_train/ +(myenv) [dshekar@lxplus956 bdt_train]$ ls +Classification_BDT.py config_BDT.py configuration_BDT.py cuts_BDT.py preselections.py __pycache__ samples_BDT.py structure.py TMVA.root +(myenv) [dshekar@lxplus956 bdt_train]$ rm samples_BDT.py +(myenv) [dshekar@lxplus956 bdt_train]$ vim samples_BDT.py +(myenv) [dshekar@lxplus956 bdt_train]$ vim structure.py +(myenv) [dshekar@lxplus956 bdt_train]$ python Classification_BDT.py +Need to query for files for folder /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +MuonEG_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Muon_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +EGamma_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +MuonEG_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Muon_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +EGamma_Run2022D-ReReco-v1 +Input arguments: ['Classification_BDT.py'] +No suffix, running with standard output name +Sample name: WZ +Sub-sample: WZTo3LNu_mllmin01 +Sub-sample: WZTo2L2Q +DataSetInfo : [dataset] : Added class "Background" + : Add Tree Events of type Background with 0 events + : Encountered empty TTree or TChain of class Background +***> abort program execution +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 82, in + runJob() + ~~~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 42, in runJob + dataloader.AddBackgroundTree(sample['tree'], 1.0) + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^ +cppyy.gbl.std.runtime_error: void TMVA::DataLoader::AddBackgroundTree(TTree* background, Double_t weight = 1., TMVA::Types::ETreeType treetype = Types::kMaxTreeType) => + runtime_error: FATAL error +(myenv) [dshekar@lxplus956 bdt_train]$ exit +logout +Connection to lxplus.cern.ch closed. +danush@danush-5 ZpTreweighting % lxplus +(dshekar@lxplus.cern.ch) Password: +(dshekar@lxplus.cern.ch) Your 2nd factor (dshekar): 942324 +* ******************************************************************** +* Welcome to lxplus968.cern.ch, Red Hat Enterprise Linux release 9.7 (Plow) +* Archive of news is available in /etc/motd-archive +* Reminder: you have agreed to the CERN +* computing rules, in particular OC5. CERN implements +* the measures necessary to ensure compliance. +* https://cern.ch/ComputingRules +* Puppet environment: qa, Roger state: production +* Foreman hostgroup: lxplus/nodes/login +* Availability zone: cern-geneva-b +* LXPLUS Public Login Service - http://lxplusdoc.web.cern.ch/ +* Please read LXPLUS Privacy Notice in http://cern.ch/go/TpV7 +* ******************************************************************** +* /!\ LxPlus now requires 2FA from outside of CERN /!\ +* All details for 2FA enforcing on https://cern.ch/otg0156449 + +[dshekar@lxplus968 ~]$ cd public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/ +[dshekar@lxplus968 zh3l_run3_bdt]$ cd bdt_train/ +[dshekar@lxplus968 bdt_train]$ rm preselections.py +[dshekar@lxplus968 bdt_train]$ vim preselections.py +[dshekar@lxplus968 bdt_train]$ vim preselections.py +[dshekar@lxplus968 bdt_train]$ cd .. +[dshekar@lxplus968 zh3l_run3_bdt]$ cd .. +[dshekar@lxplus968 ZH3l_BDT]$ cd .. +[dshekar@lxplus968 PlotsConfigurationsRun3]$ source st +-bash: st: No such file or directory +[dshekar@lxplus968 PlotsConfigurationsRun3]$ rdfenv +first source of start.sh +cd ZH(myenv) [dshekar@lxplus968 PlotsConfigurationsRun3]$ cd ZH3l_BDT/zh3l_run3_bdt/bdt_train/ +(myenv) [dshekar@lxplus968 bdt_train]$ python3 Classification_BDT.py +Need to query for files for folder /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +MuonEG_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Muon_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +EGamma_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +MuonEG_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Muon_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +EGamma_Run2022D-ReReco-v1 +Input arguments: ['Classification_BDT.py'] +No suffix, running with standard output name +Sample name: WZ +Sub-sample: WZTo3LNu_mllmin01 +Sub-sample: WZTo2L2Q +DataSetInfo : [dataset] : Added class "Background" + : Add Tree Events of type Background with 0 events + : Encountered empty TTree or TChain of class Background +***> abort program execution +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 82, in + runJob() + ~~~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 42, in runJob + dataloader.AddBackgroundTree(sample['tree'], 1.0) + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^ +cppyy.gbl.std.runtime_error: void TMVA::DataLoader::AddBackgroundTree(TTree* background, Double_t weight = 1., TMVA::Types::ETreeType treetype = Types::kMaxTreeType) => + runtime_error: FATAL error +(myenv) [dshekar@lxplus968 bdt_train]$ vim samples_BDT.py +(myenv) [dshekar@lxplus968 bdt_train]$ vim samples_BDT.py +^[[A^[[A(myenv) [dshekar@lxplus968 bdt_train]$ python3 Classification_BDT.py +Need to query for files for folder /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +Found 0 files for sample WZTo3LNu_mllmin01 +Found 0 files for sample WZTo2L2Q +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample MuonEG_Run2022C-ReReco-v1 +MuonEG_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample Muon_Run2022C-ReReco-v1 +Muon_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample EGamma_Run2022C-ReReco-v1 +EGamma_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample MuonEG_Run2022D-ReReco-v1 +MuonEG_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample Muon_Run2022D-ReReco-v1 +Muon_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample EGamma_Run2022D-ReReco-v1 +EGamma_Run2022D-ReReco-v1 +Input arguments: ['Classification_BDT.py'] +No suffix, running with standard output name +Sample name: WZ +Sub-sample: WZTo3LNu_mllmin01 +Sub-sample: WZTo2L2Q +DataSetInfo : [dataset] : Added class "Background" + : Add Tree Events of type Background with 0 events + : Encountered empty TTree or TChain of class Background +***> abort program execution +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 82, in + runJob() + ~~~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/PlotsConfigurationsRun3/ZH3l_BDT/zh3l_run3_bdt/bdt_train/Classification_BDT.py", line 42, in runJob + dataloader.AddBackgroundTree(sample['tree'], 1.0) + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^ +cppyy.gbl.std.runtime_error: void TMVA::DataLoader::AddBackgroundTree(TTree* background, Double_t weight = 1., TMVA::Types::ETreeType treetype = Types::kMaxTreeType) => + runtime_error: FATAL error +(myenv) [dshekar@lxplus968 bdt_train]$ vim samples_BDT.py +(myenv) [dshekar@lxplus968 bdt_train]$ python3 Classification_BDT.py +Need to query for files for folder /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +Found 0 files for sample WZTo2L2Q +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample MuonEG_Run2022C-ReReco-v1 +MuonEG_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample Muon_Run2022C-ReReco-v1 +Muon_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample EGamma_Run2022C-ReReco-v1 +EGamma_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample MuonEG_Run2022D-ReReco-v1 +MuonEG_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample Muon_Run2022D-ReReco-v1 +Muon_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample EGamma_Run2022D-ReReco-v1 +EGamma_Run2022D-ReReco-v1 +Input arguments: ['Classification_BDT.py'] +No suffix, running with standard output name +Sample name: WZ +Sub-sample: WZTo3LNu +file: /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/nanoLatino_WZTo3LNu__part0.root +Sub-sample: WZTo2L2Q +DataSetInfo : [dataset] : Added class "Background" +TClass::Init:0: RuntimeWarning: no dictionary for class edm::Hash<1> is available +TClass::Init:0: RuntimeWarning: no dictionary for class edm::ProcessHistory is available +TClass::Init:0: RuntimeWarning: no dictionary for class edm::ProcessConfiguration is available +TClass::Init:0: RuntimeWarning: no dictionary for class edm::ParameterSetBlob is available +TClass::Init:0: RuntimeWarning: no dictionary for class pair,edm::ParameterSetBlob> is available + : Add Tree Events of type Background with 192231 events +Sample name: ZZ +Sub-sample: ZZ +file: /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/nanoLatino_ZZ__part0.root + : Add Tree Events of type Background with 1312 events +Sample name: ggZH_hww +Sub-sample: GluGluZH_Zto2L_Hto2WtoLNu2Q +file: /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/nanoLatino_GluGluZH_Zto2L_Hto2WtoLNu2Q__part0.root +DataSetInfo : [dataset] : Added class "Signal" + : Add Tree Events of type Signal with 8766 events + : Dataset[dataset] : Class index : 0 name : Background + : Dataset[dataset] : Class index : 1 name : Signal +Factory : Booking method: gBDT_D2 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray + : Rebuilding Dataset dataset + : Building event vectors for type 2 Background + : Dataset[dataset] : create input formulas for tree Events + : Dataset[dataset] : create input formulas for tree Events +^C^C^C : Building event vectors for type 2 Background + : Dataset[dataset] : create input formulas for tree Events + : Dataset[dataset] : create input formulas for tree Events + : Building event vectors for type 2 Signal + : Dataset[dataset] : create input formulas for tree Events + : Dataset[dataset] : create input formulas for tree Events + : Found events with NaN and/or +-inf values + : Dataset[dataset] : Input expression resolves to infinite value (+inf or -inf): Alt$(Lepton_pt[0],0) (2 times) + : Dataset[dataset] : Input expression resolves to infinite value (+inf or -inf): Alt$(Lepton_pt[1],0) (2 times) + : Dataset[dataset] : Input expression resolves to infinite value (+inf or -inf): PuppiMET_pt (4 times) + : These NaN and/or +-infs were all removed by the specified cut, continuing. + : +DataSetFactory : [dataset] : Number of events in input trees + : Dataset[dataset] : Background requirement: "((1) && (Alt$( Lepton_pt[0], 0) > 25 && Alt$( Lepton_pt[1], 0) > 20 && Alt$( Lepton_pt[2], 0) > 15 && Alt$( Lepton_pt[3], 0) < 10 ))" + : Dataset[dataset] : Background -- number of events passed: 29350 / sum of weights: 29350 + : Dataset[dataset] : Background -- efficiency : 0.151646 + : Dataset[dataset] : Signal requirement: "((1) && (Alt$( Lepton_pt[0], 0) > 25 && Alt$( Lepton_pt[1], 0) > 20 && Alt$( Lepton_pt[2], 0) > 15 && Alt$( Lepton_pt[3], 0) < 10 ))" + : Dataset[dataset] : Signal -- number of events passed: 2149 / sum of weights: 2149 + : Dataset[dataset] : Signal -- efficiency : 0.245152 + : Dataset[dataset] : you have opted for interpreting the requested number of training/testing events + : to be the number of events AFTER your preselection cuts + : + : Dataset[dataset] : you have opted for interpreting the requested number of training/testing events + : to be the number of events AFTER your preselection cuts + : + : Number of training and testing events + : --------------------------------------------------------------------------- + : Background -- training events : 14675 + : Background -- testing events : 14675 + : Background -- training and testing events: 29350 + : Dataset[dataset] : Background -- due to the preselection a scaling factor has been applied to the numbers of requested events: 0.151646 + : Signal -- training events : 1074 + : Signal -- testing events : 1074 + : Signal -- training and testing events: 2148 + : Dataset[dataset] : Signal -- due to the preselection a scaling factor has been applied to the numbers of requested events: 0.245152 + : +DataSetInfo : Correlation matrix (Background): + : ----------------------------------------------------------------------------------------------------------------------------------------- + : Alt__CleanJet_pt_0_,0_ ZH3l_dphilmetjj PuppiMET_pt Alt__Lepton_pt_0_,0_ Alt__Lepton_pt_1_,0_ Alt__Lepton_pt_2_,0_ + : Alt__CleanJet_pt_0_,0_: +1.000 +0.457 +0.380 +0.590 +0.349 +0.217 + : ZH3l_dphilmetjj: +0.457 +1.000 +0.196 +0.276 +0.169 +0.102 + : PuppiMET_pt: +0.380 +0.196 +1.000 +0.297 +0.131 +0.086 + : Alt__Lepton_pt_0_,0_: +0.590 +0.276 +0.297 +1.000 +0.667 +0.377 + : Alt__Lepton_pt_1_,0_: +0.349 +0.169 +0.131 +0.667 +1.000 +0.562 + : Alt__Lepton_pt_2_,0_: +0.217 +0.102 +0.086 +0.377 +0.562 +1.000 + : ----------------------------------------------------------------------------------------------------------------------------------------- +DataSetInfo : Correlation matrix (Signal): + : ----------------------------------------------------------------------------------------------------------------------------------------- + : Alt__CleanJet_pt_0_,0_ ZH3l_dphilmetjj PuppiMET_pt Alt__Lepton_pt_0_,0_ Alt__Lepton_pt_1_,0_ Alt__Lepton_pt_2_,0_ + : Alt__CleanJet_pt_0_,0_: +1.000 +0.403 +0.191 +0.405 +0.206 +0.073 + : ZH3l_dphilmetjj: +0.403 +1.000 -0.020 +0.186 +0.042 -0.022 + : PuppiMET_pt: +0.191 -0.020 +1.000 +0.242 +0.116 +0.012 + : Alt__Lepton_pt_0_,0_: +0.405 +0.186 +0.242 +1.000 +0.475 +0.282 + : Alt__Lepton_pt_1_,0_: +0.206 +0.042 +0.116 +0.475 +1.000 +0.581 + : Alt__Lepton_pt_2_,0_: +0.073 -0.022 +0.012 +0.282 +0.581 +1.000 + : ----------------------------------------------------------------------------------------------------------------------------------------- +DataSetFactory : [dataset] : + : +Factory : Booking method: gBDT_D2_S01 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Booking method: gBDT_D2_C300 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Booking method: gBDT_D3 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Booking method: gBDT_D4 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Booking method: gBDT_D5 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Booking method: gBDT_D6 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Train all methods +Factory : [dataset] : Create Transformation "I" with events from all classes. + : + : Transformation, Variable selection : + : Input : variable 'Alt$(CleanJet_pt[0],0)' <---> Output : variable 'Alt$(CleanJet_pt[0],0)' + : Input : variable 'ZH3l_dphilmetjj' <---> Output : variable 'ZH3l_dphilmetjj' + : Input : variable 'PuppiMET_pt' <---> Output : variable 'PuppiMET_pt' + : Input : variable 'Alt$(Lepton_pt[0],0)' <---> Output : variable 'Alt$(Lepton_pt[0],0)' + : Input : variable 'Alt$(Lepton_pt[1],0)' <---> Output : variable 'Alt$(Lepton_pt[1],0)' + : Input : variable 'Alt$(Lepton_pt[2],0)' <---> Output : variable 'Alt$(Lepton_pt[2],0)' +TFHandler_Factory : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 64.261 86.435 [ 0.0000 1574.9 ] + : ZH3l_dphilmetjj: -7890.0 4079.7 [ -9999.0 3.1416 ] + : PuppiMET_pt: 58.493 45.723 [ 0.26237 982.78 ] + : Alt$(Lepton_pt[0],0): 87.375 56.969 [ 25.119 1177.6 ] + : Alt$(Lepton_pt[1],0): 53.660 30.721 [ 20.024 684.17 ] + : Alt$(Lepton_pt[2],0): 32.169 15.747 [ 15.004 230.21 ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Ranking input variables (method unspecific)... +IdTransformation : Ranking result (top variable is best ranked) + : ----------------------------------------------- + : Rank : Variable : Separation + : ----------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 1.781e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.681e-01 + : 3 : ZH3l_dphilmetjj : 1.549e-01 + : 4 : Alt$(Lepton_pt[1],0) : 8.785e-02 + : 5 : Alt$(Lepton_pt[2],0) : 4.566e-02 + : 6 : PuppiMET_pt : 1.175e-02 + : ----------------------------------------------- +Factory : Train method: gBDT_D2 for Classification + : +gBDT_D2 : #events: (reweighted) sig: 7874.5 bkg: 7874.5 + : #events: (unweighted) sig: 1074 bkg: 14675 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 15749 events: 1.18 sec +gBDT_D2 : [dataset] : Evaluation of gBDT_D2 on training sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.128 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D2.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D2.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D2 +Factory : Training finished + : +Factory : Train method: gBDT_D2_S01 for Classification + : +gBDT_D2_S01 : #events: (reweighted) sig: 7874.5 bkg: 7874.5 + : #events: (unweighted) sig: 1074 bkg: 14675 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 15749 events: 1.3 sec +gBDT_D2_S01 : [dataset] : Evaluation of gBDT_D2_S01 on training sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.118 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D2_S01.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D2_S01.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D2_S01 +Factory : Training finished + : +Factory : Train method: gBDT_D2_C300 for Classification + : +gBDT_D2_C300 : #events: (reweighted) sig: 7874.5 bkg: 7874.5 + : #events: (unweighted) sig: 1074 bkg: 14675 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 15749 events: 1.23 sec +gBDT_D2_C300 : [dataset] : Evaluation of gBDT_D2_C300 on training sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.126 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D2_C300.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D2_C300.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D2_C300 +Factory : Training finished + : +Factory : Train method: gBDT_D3 for Classification + : +gBDT_D3 : #events: (reweighted) sig: 7874.5 bkg: 7874.5 + : #events: (unweighted) sig: 1074 bkg: 14675 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 15749 events: 1.67 sec +gBDT_D3 : [dataset] : Evaluation of gBDT_D3 on training sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.177 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D3.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D3.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D3 +Factory : Training finished + : +Factory : Train method: gBDT_D4 for Classification + : +gBDT_D4 : #events: (reweighted) sig: 7874.5 bkg: 7874.5 + : #events: (unweighted) sig: 1074 bkg: 14675 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 15749 events: 2.11 sec +gBDT_D4 : [dataset] : Evaluation of gBDT_D4 on training sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.262 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D4.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D4.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D4 +Factory : Training finished + : +Factory : Train method: gBDT_D5 for Classification + : +gBDT_D5 : #events: (reweighted) sig: 7874.5 bkg: 7874.5 + : #events: (unweighted) sig: 1074 bkg: 14675 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 15749 events: 2.52 sec +gBDT_D5 : [dataset] : Evaluation of gBDT_D5 on training sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.33 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D5.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D5.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D5 +Factory : Training finished + : +Factory : Train method: gBDT_D6 for Classification + : +gBDT_D6 : #events: (reweighted) sig: 7874.5 bkg: 7874.5 + : #events: (unweighted) sig: 1074 bkg: 14675 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 15749 events: 2.83 sec +gBDT_D6 : [dataset] : Evaluation of gBDT_D6 on training sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.429 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D6.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D6.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D6 +Factory : Training finished + : + : Ranking input variables (method specific)... +gBDT_D2 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 2.054e-01 + : 2 : PuppiMET_pt : 1.873e-01 + : 3 : Alt$(Lepton_pt[1],0) : 1.805e-01 + : 4 : Alt$(Lepton_pt[0],0) : 1.739e-01 + : 5 : Alt$(Lepton_pt[2],0) : 1.559e-01 + : 6 : ZH3l_dphilmetjj : 9.705e-02 + : -------------------------------------------------------- +gBDT_D2_S01 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 2.375e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.989e-01 + : 3 : ZH3l_dphilmetjj : 1.787e-01 + : 4 : Alt$(Lepton_pt[1],0) : 1.350e-01 + : 5 : PuppiMET_pt : 1.314e-01 + : 6 : Alt$(Lepton_pt[2],0) : 1.185e-01 + : -------------------------------------------------------- +gBDT_D2_C300 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 2.046e-01 + : 2 : PuppiMET_pt : 1.858e-01 + : 3 : Alt$(Lepton_pt[0],0) : 1.782e-01 + : 4 : Alt$(Lepton_pt[1],0) : 1.768e-01 + : 5 : Alt$(Lepton_pt[2],0) : 1.584e-01 + : 6 : ZH3l_dphilmetjj : 9.616e-02 + : -------------------------------------------------------- +gBDT_D3 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 2.054e-01 + : 2 : PuppiMET_pt : 1.846e-01 + : 3 : Alt$(Lepton_pt[1],0) : 1.774e-01 + : 4 : Alt$(Lepton_pt[2],0) : 1.709e-01 + : 5 : Alt$(Lepton_pt[0],0) : 1.704e-01 + : 6 : ZH3l_dphilmetjj : 9.124e-02 + : -------------------------------------------------------- +gBDT_D4 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 1.988e-01 + : 2 : PuppiMET_pt : 1.838e-01 + : 3 : Alt$(Lepton_pt[0],0) : 1.827e-01 + : 4 : Alt$(Lepton_pt[1],0) : 1.770e-01 + : 5 : Alt$(Lepton_pt[2],0) : 1.724e-01 + : 6 : ZH3l_dphilmetjj : 8.531e-02 + : -------------------------------------------------------- +gBDT_D5 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 2.077e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.847e-01 + : 3 : PuppiMET_pt : 1.808e-01 + : 4 : Alt$(Lepton_pt[1],0) : 1.771e-01 + : 5 : Alt$(Lepton_pt[2],0) : 1.712e-01 + : 6 : ZH3l_dphilmetjj : 7.854e-02 + : -------------------------------------------------------- +gBDT_D6 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 2.054e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.909e-01 + : 3 : PuppiMET_pt : 1.818e-01 + : 4 : Alt$(Lepton_pt[1],0) : 1.750e-01 + : 5 : Alt$(Lepton_pt[2],0) : 1.723e-01 + : 6 : ZH3l_dphilmetjj : 7.473e-02 + : -------------------------------------------------------- +Factory : === Destroy and recreate all methods via weight files for testing === + : + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D2.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D2_S01.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D2_C300.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D3.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D4.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D5.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D6.weights.xml +Factory : Test all methods +Factory : Test method: gBDT_D2 for Classification performance + : +gBDT_D2 : [dataset] : Evaluation of gBDT_D2 on testing sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.12 sec +Factory : Test method: gBDT_D2_S01 for Classification performance + : +gBDT_D2_S01 : [dataset] : Evaluation of gBDT_D2_S01 on testing sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.12 sec +Factory : Test method: gBDT_D2_C300 for Classification performance + : +gBDT_D2_C300 : [dataset] : Evaluation of gBDT_D2_C300 on testing sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.126 sec +Factory : Test method: gBDT_D3 for Classification performance + : +gBDT_D3 : [dataset] : Evaluation of gBDT_D3 on testing sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.171 sec +Factory : Test method: gBDT_D4 for Classification performance + : +gBDT_D4 : [dataset] : Evaluation of gBDT_D4 on testing sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.244 sec +Factory : Test method: gBDT_D5 for Classification performance + : +gBDT_D5 : [dataset] : Evaluation of gBDT_D5 on testing sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.324 sec +Factory : Test method: gBDT_D6 for Classification performance + : +gBDT_D6 : [dataset] : Evaluation of gBDT_D6 on testing sample (15749 events) + : Elapsed time for evaluation of 15749 events: 0.388 sec +Factory : Evaluate all methods +Factory : Evaluate classifier: gBDT_D2 + : +gBDT_D2 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D2 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 64.510 86.123 [ 0.0000 1266.3 ] + : ZH3l_dphilmetjj: -7857.0 4102.9 [ -9999.0 3.1412 ] + : PuppiMET_pt: 58.868 46.470 [ 0.35355 961.59 ] + : Alt$(Lepton_pt[0],0): 87.324 55.541 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.827 31.224 [ 20.008 526.97 ] + : Alt$(Lepton_pt[2],0): 32.209 16.318 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D2_S01 + : +gBDT_D2_S01 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D2_S01 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 64.510 86.123 [ 0.0000 1266.3 ] + : ZH3l_dphilmetjj: -7857.0 4102.9 [ -9999.0 3.1412 ] + : PuppiMET_pt: 58.868 46.470 [ 0.35355 961.59 ] + : Alt$(Lepton_pt[0],0): 87.324 55.541 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.827 31.224 [ 20.008 526.97 ] + : Alt$(Lepton_pt[2],0): 32.209 16.318 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D2_C300 + : +gBDT_D2_C300 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D2_C300 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 64.510 86.123 [ 0.0000 1266.3 ] + : ZH3l_dphilmetjj: -7857.0 4102.9 [ -9999.0 3.1412 ] + : PuppiMET_pt: 58.868 46.470 [ 0.35355 961.59 ] + : Alt$(Lepton_pt[0],0): 87.324 55.541 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.827 31.224 [ 20.008 526.97 ] + : Alt$(Lepton_pt[2],0): 32.209 16.318 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D3 + : +gBDT_D3 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D3 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 64.510 86.123 [ 0.0000 1266.3 ] + : ZH3l_dphilmetjj: -7857.0 4102.9 [ -9999.0 3.1412 ] + : PuppiMET_pt: 58.868 46.470 [ 0.35355 961.59 ] + : Alt$(Lepton_pt[0],0): 87.324 55.541 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.827 31.224 [ 20.008 526.97 ] + : Alt$(Lepton_pt[2],0): 32.209 16.318 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D4 + : +gBDT_D4 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D4 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 64.510 86.123 [ 0.0000 1266.3 ] + : ZH3l_dphilmetjj: -7857.0 4102.9 [ -9999.0 3.1412 ] + : PuppiMET_pt: 58.868 46.470 [ 0.35355 961.59 ] + : Alt$(Lepton_pt[0],0): 87.324 55.541 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.827 31.224 [ 20.008 526.97 ] + : Alt$(Lepton_pt[2],0): 32.209 16.318 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D5 + : +gBDT_D5 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D5 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 64.510 86.123 [ 0.0000 1266.3 ] + : ZH3l_dphilmetjj: -7857.0 4102.9 [ -9999.0 3.1412 ] + : PuppiMET_pt: 58.868 46.470 [ 0.35355 961.59 ] + : Alt$(Lepton_pt[0],0): 87.324 55.541 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.827 31.224 [ 20.008 526.97 ] + : Alt$(Lepton_pt[2],0): 32.209 16.318 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D6 + : +gBDT_D6 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D6 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 64.510 86.123 [ 0.0000 1266.3 ] + : ZH3l_dphilmetjj: -7857.0 4102.9 [ -9999.0 3.1412 ] + : PuppiMET_pt: 58.868 46.470 [ 0.35355 961.59 ] + : Alt$(Lepton_pt[0],0): 87.324 55.541 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.827 31.224 [ 20.008 526.97 ] + : Alt$(Lepton_pt[2],0): 32.209 16.318 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- + : + : Evaluation results ranked by best signal efficiency and purity (area) + : ------------------------------------------------------------------------------------------------------------------- + : DataSet MVA + : Name: Method: ROC-integ + : dataset gBDT_D2_S01 : 0.844 + : dataset gBDT_D2_C300 : 0.841 + : dataset gBDT_D2 : 0.841 + : dataset gBDT_D3 : 0.839 + : dataset gBDT_D4 : 0.831 + : dataset gBDT_D5 : 0.830 + : dataset gBDT_D6 : 0.826 + : ------------------------------------------------------------------------------------------------------------------- + : + : Testing efficiency compared to training efficiency (overtraining check) + : ------------------------------------------------------------------------------------------------------------------- + : DataSet MVA Signal efficiency: from test sample (from training sample) + : Name: Method: @B=0.01 @B=0.10 @B=0.30 + : ------------------------------------------------------------------------------------------------------------------- + : dataset gBDT_D2_S01 : 0.152 (0.166) 0.516 (0.519) 0.826 (0.823) + : dataset gBDT_D2_C300 : 0.141 (0.146) 0.507 (0.514) 0.820 (0.821) + : dataset gBDT_D2 : 0.133 (0.139) 0.503 (0.514) 0.819 (0.822) + : dataset gBDT_D3 : 0.137 (0.156) 0.514 (0.536) 0.815 (0.820) + : dataset gBDT_D4 : 0.132 (0.148) 0.507 (0.535) 0.789 (0.808) + : dataset gBDT_D5 : 0.135 (0.161) 0.502 (0.542) 0.787 (0.807) + : dataset gBDT_D6 : 0.124 (0.155) 0.502 (0.544) 0.777 (0.792) + : ------------------------------------------------------------------------------------------------------------------- + : +Dataset:dataset : Created tree 'TestTree' with 15749 events + : +Dataset:dataset : Created tree 'TrainTree' with 15749 events + : +Factory : Thank you for using TMVA! + : For citation information, please visit: http://tmva.sf.net/citeTMVA.html +(myenv) [dshekar@lxplus968 bdt_train]$ vim samples_BDT.py +(myenv) [dshekar@lxplus968 bdt_train]$ vim samples_BDT.py +(myenv) [dshekar@lxplus968 bdt_train]$ python3 Classification_BDT.py +Need to query for files for folder /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +Found 0 files for sample WZTo2L2Q +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample MuonEG_Run2022C-ReReco-v1 +MuonEG_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample Muon_Run2022C-ReReco-v1 +Muon_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample EGamma_Run2022C-ReReco-v1 +EGamma_Run2022C-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample MuonEG_Run2022D-ReReco-v1 +MuonEG_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample Muon_Run2022D-ReReco-v1 +Muon_Run2022D-ReReco-v1 +Need to query for files for folder /eos/cms/store/group/phys_higgs/cmshww/amassiro/HWWNano/Run2022_ReReco_nAODv12_Full2022v12/DATAl2loose2022v12__l2tight/ +Found 0 files for sample EGamma_Run2022D-ReReco-v1 +EGamma_Run2022D-ReReco-v1 +Input arguments: ['Classification_BDT.py'] +No suffix, running with standard output name +Sample name: WZ +Sub-sample: WZTo3LNu +file: /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/nanoLatino_WZTo3LNu__part0.root +Sub-sample: WZTo2L2Q +Sub-sample: WZ +file: /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/nanoLatino_WZ__part0.root +DataSetInfo : [dataset] : Added class "Background" +TClass::Init:0: RuntimeWarning: no dictionary for class edm::Hash<1> is available +TClass::Init:0: RuntimeWarning: no dictionary for class edm::ProcessHistory is available +TClass::Init:0: RuntimeWarning: no dictionary for class edm::ProcessConfiguration is available +TClass::Init:0: RuntimeWarning: no dictionary for class edm::ParameterSetBlob is available +TClass::Init:0: RuntimeWarning: no dictionary for class pair,edm::ParameterSetBlob> is available + : Add Tree Events of type Background with 241514 events +Sample name: ZZ +Sub-sample: ZZ +file: /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/nanoLatino_ZZ__part0.root + : Add Tree Events of type Background with 1312 events +Sample name: ggZH_hww +Sub-sample: GluGluZH_Zto2L_Hto2WtoLNu2Q +file: /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/nanoLatino_GluGluZH_Zto2L_Hto2WtoLNu2Q__part0.root +DataSetInfo : [dataset] : Added class "Signal" + : Add Tree Events of type Signal with 8766 events + : Dataset[dataset] : Class index : 0 name : Background + : Dataset[dataset] : Class index : 1 name : Signal +Factory : Booking method: gBDT_D2 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray + : Rebuilding Dataset dataset + : Building event vectors for type 2 Background + : Dataset[dataset] : create input formulas for tree Events + : Dataset[dataset] : create input formulas for tree Events + : Dataset[dataset] : create input formulas for tree Events + : Building event vectors for type 2 Background + : Dataset[dataset] : create input formulas for tree Events + : Dataset[dataset] : create input formulas for tree Events + : Building event vectors for type 2 Signal + : Dataset[dataset] : create input formulas for tree Events + : Dataset[dataset] : create input formulas for tree Events + : Found events with NaN and/or +-inf values + : Dataset[dataset] : Input expression resolves to infinite value (+inf or -inf): Alt$(Lepton_pt[0],0) (2 times) + : Dataset[dataset] : Input expression resolves to infinite value (+inf or -inf): Alt$(Lepton_pt[1],0) (2 times) + : Dataset[dataset] : Input expression resolves to infinite value (+inf or -inf): PuppiMET_pt (4 times) + : These NaN and/or +-infs were all removed by the specified cut, continuing. + : +DataSetFactory : [dataset] : Number of events in input trees + : Dataset[dataset] : Background requirement: "((1) && (Alt$( Lepton_pt[0], 0) > 25 && Alt$( Lepton_pt[1], 0) > 20 && Alt$( Lepton_pt[2], 0) > 15 && Alt$( Lepton_pt[3], 0) < 10 ))" + : Dataset[dataset] : Background -- number of events passed: 32615 / sum of weights: 32615 + : Dataset[dataset] : Background -- efficiency : 0.134314 + : Dataset[dataset] : Signal requirement: "((1) && (Alt$( Lepton_pt[0], 0) > 25 && Alt$( Lepton_pt[1], 0) > 20 && Alt$( Lepton_pt[2], 0) > 15 && Alt$( Lepton_pt[3], 0) < 10 ))" + : Dataset[dataset] : Signal -- number of events passed: 2149 / sum of weights: 2149 + : Dataset[dataset] : Signal -- efficiency : 0.245152 + : Dataset[dataset] : you have opted for interpreting the requested number of training/testing events + : to be the number of events AFTER your preselection cuts + : + : Dataset[dataset] : you have opted for interpreting the requested number of training/testing events + : to be the number of events AFTER your preselection cuts + : + : Number of training and testing events + : --------------------------------------------------------------------------- + : Background -- training events : 16307 + : Background -- testing events : 16307 + : Background -- training and testing events: 32614 + : Dataset[dataset] : Background -- due to the preselection a scaling factor has been applied to the numbers of requested events: 0.134314 + : Signal -- training events : 1074 + : Signal -- testing events : 1074 + : Signal -- training and testing events: 2148 + : Dataset[dataset] : Signal -- due to the preselection a scaling factor has been applied to the numbers of requested events: 0.245152 + : +DataSetInfo : Correlation matrix (Background): + : ----------------------------------------------------------------------------------------------------------------------------------------- + : Alt__CleanJet_pt_0_,0_ ZH3l_dphilmetjj PuppiMET_pt Alt__Lepton_pt_0_,0_ Alt__Lepton_pt_1_,0_ Alt__Lepton_pt_2_,0_ + : Alt__CleanJet_pt_0_,0_: +1.000 +0.464 +0.390 +0.578 +0.334 +0.211 + : ZH3l_dphilmetjj: +0.464 +1.000 +0.198 +0.281 +0.167 +0.101 + : PuppiMET_pt: +0.390 +0.198 +1.000 +0.296 +0.128 +0.065 + : Alt__Lepton_pt_0_,0_: +0.578 +0.281 +0.296 +1.000 +0.666 +0.363 + : Alt__Lepton_pt_1_,0_: +0.334 +0.167 +0.128 +0.666 +1.000 +0.559 + : Alt__Lepton_pt_2_,0_: +0.211 +0.101 +0.065 +0.363 +0.559 +1.000 + : ----------------------------------------------------------------------------------------------------------------------------------------- +DataSetInfo : Correlation matrix (Signal): + : ----------------------------------------------------------------------------------------------------------------------------------------- + : Alt__CleanJet_pt_0_,0_ ZH3l_dphilmetjj PuppiMET_pt Alt__Lepton_pt_0_,0_ Alt__Lepton_pt_1_,0_ Alt__Lepton_pt_2_,0_ + : Alt__CleanJet_pt_0_,0_: +1.000 +0.393 +0.108 +0.405 +0.251 +0.071 + : ZH3l_dphilmetjj: +0.393 +1.000 +0.016 +0.184 +0.073 -0.026 + : PuppiMET_pt: +0.108 +0.016 +1.000 +0.287 +0.127 -0.015 + : Alt__Lepton_pt_0_,0_: +0.405 +0.184 +0.287 +1.000 +0.475 +0.247 + : Alt__Lepton_pt_1_,0_: +0.251 +0.073 +0.127 +0.475 +1.000 +0.571 + : Alt__Lepton_pt_2_,0_: +0.071 -0.026 -0.015 +0.247 +0.571 +1.000 + : ----------------------------------------------------------------------------------------------------------------------------------------- +DataSetFactory : [dataset] : + : +Factory : Booking method: gBDT_D2_S01 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Booking method: gBDT_D2_C300 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Booking method: gBDT_D3 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Booking method: gBDT_D4 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Booking method: gBDT_D5 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Booking method: gBDT_D6 + : + : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad + : --> change to new default NegWeightTreatment=Pray +Factory : Train all methods +Factory : [dataset] : Create Transformation "I" with events from all classes. + : + : Transformation, Variable selection : + : Input : variable 'Alt$(CleanJet_pt[0],0)' <---> Output : variable 'Alt$(CleanJet_pt[0],0)' + : Input : variable 'ZH3l_dphilmetjj' <---> Output : variable 'ZH3l_dphilmetjj' + : Input : variable 'PuppiMET_pt' <---> Output : variable 'PuppiMET_pt' + : Input : variable 'Alt$(Lepton_pt[0],0)' <---> Output : variable 'Alt$(Lepton_pt[0],0)' + : Input : variable 'Alt$(Lepton_pt[1],0)' <---> Output : variable 'Alt$(Lepton_pt[1],0)' + : Input : variable 'Alt$(Lepton_pt[2],0)' <---> Output : variable 'Alt$(Lepton_pt[2],0)' +TFHandler_Factory : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 61.376 85.017 [ 0.0000 1574.9 ] + : ZH3l_dphilmetjj: -7990.8 4006.4 [ -9999.0 3.1412 ] + : PuppiMET_pt: 58.155 45.458 [ 0.68268 982.78 ] + : Alt$(Lepton_pt[0],0): 86.236 54.880 [ 25.017 1177.6 ] + : Alt$(Lepton_pt[1],0): 53.167 29.659 [ 20.024 522.93 ] + : Alt$(Lepton_pt[2],0): 32.062 15.353 [ 15.000 238.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Ranking input variables (method unspecific)... +IdTransformation : Ranking result (top variable is best ranked) + : ----------------------------------------------- + : Rank : Variable : Separation + : ----------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 1.997e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.891e-01 + : 3 : ZH3l_dphilmetjj : 1.564e-01 + : 4 : Alt$(Lepton_pt[1],0) : 1.078e-01 + : 5 : Alt$(Lepton_pt[2],0) : 5.128e-02 + : 6 : PuppiMET_pt : 1.343e-02 + : ----------------------------------------------- +Factory : Train method: gBDT_D2 for Classification + : +gBDT_D2 : #events: (reweighted) sig: 8690.5 bkg: 8690.5 + : #events: (unweighted) sig: 1074 bkg: 16307 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 17381 events: 1.24 sec +gBDT_D2 : [dataset] : Evaluation of gBDT_D2 on training sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.141 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D2.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D2.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D2 +Factory : Training finished + : +Factory : Train method: gBDT_D2_S01 for Classification + : +gBDT_D2_S01 : #events: (reweighted) sig: 8690.5 bkg: 8690.5 + : #events: (unweighted) sig: 1074 bkg: 16307 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 17381 events: 1.46 sec +gBDT_D2_S01 : [dataset] : Evaluation of gBDT_D2_S01 on training sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.167 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D2_S01.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D2_S01.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D2_S01 +Factory : Training finished + : +Factory : Train method: gBDT_D2_C300 for Classification + : +gBDT_D2_C300 : #events: (reweighted) sig: 8690.5 bkg: 8690.5 + : #events: (unweighted) sig: 1074 bkg: 16307 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 17381 events: 1.4 sec +gBDT_D2_C300 : [dataset] : Evaluation of gBDT_D2_C300 on training sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.161 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D2_C300.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D2_C300.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D2_C300 +Factory : Training finished + : +Factory : Train method: gBDT_D3 for Classification + : +gBDT_D3 : #events: (reweighted) sig: 8690.5 bkg: 8690.5 + : #events: (unweighted) sig: 1074 bkg: 16307 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 17381 events: 1.89 sec +gBDT_D3 : [dataset] : Evaluation of gBDT_D3 on training sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.203 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D3.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D3.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D3 +Factory : Training finished + : +Factory : Train method: gBDT_D4 for Classification + : +gBDT_D4 : #events: (reweighted) sig: 8690.5 bkg: 8690.5 + : #events: (unweighted) sig: 1074 bkg: 16307 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 17381 events: 2.33 sec +gBDT_D4 : [dataset] : Evaluation of gBDT_D4 on training sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.277 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D4.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D4.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D4 +Factory : Training finished + : +Factory : Train method: gBDT_D5 for Classification + : +gBDT_D5 : #events: (reweighted) sig: 8690.5 bkg: 8690.5 + : #events: (unweighted) sig: 1074 bkg: 16307 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 17381 events: 2.63 sec +gBDT_D5 : [dataset] : Evaluation of gBDT_D5 on training sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.377 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D5.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D5.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D5 +Factory : Training finished + : +Factory : Train method: gBDT_D6 for Classification + : +gBDT_D6 : #events: (reweighted) sig: 8690.5 bkg: 8690.5 + : #events: (unweighted) sig: 1074 bkg: 16307 + : Training 500 Decision Trees ... patience please + : Elapsed time for training with 17381 events: 3.16 sec +gBDT_D6 : [dataset] : Evaluation of gBDT_D6 on training sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.46 sec + : Creating xml weight file: dataset/weights/TMVAClassification_gBDT_D6.weights.xml + : Creating standalone class: dataset/weights/TMVAClassification_gBDT_D6.class.C + : TMVA.root:/dataset/Method_BDT/gBDT_D6 +Factory : Training finished + : + : Ranking input variables (method specific)... +gBDT_D2 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 1.991e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.885e-01 + : 3 : Alt$(Lepton_pt[1],0) : 1.768e-01 + : 4 : Alt$(Lepton_pt[2],0) : 1.733e-01 + : 5 : PuppiMET_pt : 1.701e-01 + : 6 : ZH3l_dphilmetjj : 9.223e-02 + : -------------------------------------------------------- +gBDT_D2_S01 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 2.344e-01 + : 2 : Alt$(Lepton_pt[0],0) : 2.074e-01 + : 3 : ZH3l_dphilmetjj : 1.737e-01 + : 4 : PuppiMET_pt : 1.358e-01 + : 5 : Alt$(Lepton_pt[1],0) : 1.320e-01 + : 6 : Alt$(Lepton_pt[2],0) : 1.166e-01 + : -------------------------------------------------------- +gBDT_D2_C300 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 1.969e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.829e-01 + : 3 : Alt$(Lepton_pt[1],0) : 1.791e-01 + : 4 : PuppiMET_pt : 1.749e-01 + : 5 : Alt$(Lepton_pt[2],0) : 1.706e-01 + : 6 : ZH3l_dphilmetjj : 9.559e-02 + : -------------------------------------------------------- +gBDT_D3 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 1.993e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.901e-01 + : 3 : Alt$(Lepton_pt[1],0) : 1.811e-01 + : 4 : Alt$(Lepton_pt[2],0) : 1.729e-01 + : 5 : PuppiMET_pt : 1.689e-01 + : 6 : ZH3l_dphilmetjj : 8.779e-02 + : -------------------------------------------------------- +gBDT_D4 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 2.058e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.900e-01 + : 3 : Alt$(Lepton_pt[1],0) : 1.821e-01 + : 4 : Alt$(Lepton_pt[2],0) : 1.693e-01 + : 5 : PuppiMET_pt : 1.691e-01 + : 6 : ZH3l_dphilmetjj : 8.373e-02 + : -------------------------------------------------------- +gBDT_D5 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 2.136e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.952e-01 + : 3 : Alt$(Lepton_pt[1],0) : 1.816e-01 + : 4 : Alt$(Lepton_pt[2],0) : 1.679e-01 + : 5 : PuppiMET_pt : 1.646e-01 + : 6 : ZH3l_dphilmetjj : 7.716e-02 + : -------------------------------------------------------- +gBDT_D6 : Ranking result (top variable is best ranked) + : -------------------------------------------------------- + : Rank : Variable : Variable Importance + : -------------------------------------------------------- + : 1 : Alt$(CleanJet_pt[0],0) : 2.180e-01 + : 2 : Alt$(Lepton_pt[0],0) : 1.969e-01 + : 3 : Alt$(Lepton_pt[1],0) : 1.800e-01 + : 4 : PuppiMET_pt : 1.680e-01 + : 5 : Alt$(Lepton_pt[2],0) : 1.678e-01 + : 6 : ZH3l_dphilmetjj : 6.933e-02 + : -------------------------------------------------------- +Factory : === Destroy and recreate all methods via weight files for testing === + : + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D2.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D2_S01.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D2_C300.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D3.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D4.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D5.weights.xml + : Reading weight file: dataset/weights/TMVAClassification_gBDT_D6.weights.xml +Factory : Test all methods +Factory : Test method: gBDT_D2 for Classification performance + : +gBDT_D2 : [dataset] : Evaluation of gBDT_D2 on testing sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.144 sec +Factory : Test method: gBDT_D2_S01 for Classification performance + : +gBDT_D2_S01 : [dataset] : Evaluation of gBDT_D2_S01 on testing sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.132 sec +Factory : Test method: gBDT_D2_C300 for Classification performance + : +gBDT_D2_C300 : [dataset] : Evaluation of gBDT_D2_C300 on testing sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.139 sec +Factory : Test method: gBDT_D3 for Classification performance + : +gBDT_D3 : [dataset] : Evaluation of gBDT_D3 on testing sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.188 sec +Factory : Test method: gBDT_D4 for Classification performance + : +gBDT_D4 : [dataset] : Evaluation of gBDT_D4 on testing sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.257 sec +Factory : Test method: gBDT_D5 for Classification performance + : +gBDT_D5 : [dataset] : Evaluation of gBDT_D5 on testing sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.346 sec +Factory : Test method: gBDT_D6 for Classification performance + : +gBDT_D6 : [dataset] : Evaluation of gBDT_D6 on testing sample (17381 events) + : Elapsed time for evaluation of 17381 events: 0.425 sec +Factory : Evaluate all methods +Factory : Evaluate classifier: gBDT_D2 + : +gBDT_D2 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D2 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 61.434 83.138 [ 0.0000 1187.0 ] + : ZH3l_dphilmetjj: -8006.9 3994.3 [ -9999.0 3.1416 ] + : PuppiMET_pt: 58.468 45.132 [ 0.26237 961.59 ] + : Alt$(Lepton_pt[0],0): 86.235 54.980 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.415 31.130 [ 20.008 684.17 ] + : Alt$(Lepton_pt[2],0): 32.050 16.256 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D2_S01 + : +gBDT_D2_S01 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D2_S01 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 61.434 83.138 [ 0.0000 1187.0 ] + : ZH3l_dphilmetjj: -8006.9 3994.3 [ -9999.0 3.1416 ] + : PuppiMET_pt: 58.468 45.132 [ 0.26237 961.59 ] + : Alt$(Lepton_pt[0],0): 86.235 54.980 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.415 31.130 [ 20.008 684.17 ] + : Alt$(Lepton_pt[2],0): 32.050 16.256 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D2_C300 + : +gBDT_D2_C300 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D2_C300 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 61.434 83.138 [ 0.0000 1187.0 ] + : ZH3l_dphilmetjj: -8006.9 3994.3 [ -9999.0 3.1416 ] + : PuppiMET_pt: 58.468 45.132 [ 0.26237 961.59 ] + : Alt$(Lepton_pt[0],0): 86.235 54.980 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.415 31.130 [ 20.008 684.17 ] + : Alt$(Lepton_pt[2],0): 32.050 16.256 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D3 + : +gBDT_D3 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D3 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 61.434 83.138 [ 0.0000 1187.0 ] + : ZH3l_dphilmetjj: -8006.9 3994.3 [ -9999.0 3.1416 ] + : PuppiMET_pt: 58.468 45.132 [ 0.26237 961.59 ] + : Alt$(Lepton_pt[0],0): 86.235 54.980 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.415 31.130 [ 20.008 684.17 ] + : Alt$(Lepton_pt[2],0): 32.050 16.256 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D4 + : +gBDT_D4 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D4 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 61.434 83.138 [ 0.0000 1187.0 ] + : ZH3l_dphilmetjj: -8006.9 3994.3 [ -9999.0 3.1416 ] + : PuppiMET_pt: 58.468 45.132 [ 0.26237 961.59 ] + : Alt$(Lepton_pt[0],0): 86.235 54.980 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.415 31.130 [ 20.008 684.17 ] + : Alt$(Lepton_pt[2],0): 32.050 16.256 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D5 + : +gBDT_D5 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D5 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 61.434 83.138 [ 0.0000 1187.0 ] + : ZH3l_dphilmetjj: -8006.9 3994.3 [ -9999.0 3.1416 ] + : PuppiMET_pt: 58.468 45.132 [ 0.26237 961.59 ] + : Alt$(Lepton_pt[0],0): 86.235 54.980 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.415 31.130 [ 20.008 684.17 ] + : Alt$(Lepton_pt[2],0): 32.050 16.256 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- +Factory : Evaluate classifier: gBDT_D6 + : +gBDT_D6 : [dataset] : Loop over test events and fill histograms with classifier response... + : +TFHandler_gBDT_D6 : Variable Mean RMS [ Min Max ] + : --------------------------------------------------------------------------------------------------------------------------------- + : Alt$(CleanJet_pt[0],0): 61.434 83.138 [ 0.0000 1187.0 ] + : ZH3l_dphilmetjj: -8006.9 3994.3 [ -9999.0 3.1416 ] + : PuppiMET_pt: 58.468 45.132 [ 0.26237 961.59 ] + : Alt$(Lepton_pt[0],0): 86.235 54.980 [ 25.007 1132.0 ] + : Alt$(Lepton_pt[1],0): 53.415 31.130 [ 20.008 684.17 ] + : Alt$(Lepton_pt[2],0): 32.050 16.256 [ 15.000 447.13 ] + : --------------------------------------------------------------------------------------------------------------------------------- + : + : Evaluation results ranked by best signal efficiency and purity (area) + : ------------------------------------------------------------------------------------------------------------------- + : DataSet MVA + : Name: Method: ROC-integ + : dataset gBDT_D2_S01 : 0.844 + : dataset gBDT_D2_C300 : 0.843 + : dataset gBDT_D2 : 0.842 + : dataset gBDT_D3 : 0.840 + : dataset gBDT_D4 : 0.838 + : dataset gBDT_D5 : 0.835 + : dataset gBDT_D6 : 0.833 + : ------------------------------------------------------------------------------------------------------------------- + : + : Testing efficiency compared to training efficiency (overtraining check) + : ------------------------------------------------------------------------------------------------------------------- + : DataSet MVA Signal efficiency: from test sample (from training sample) + : Name: Method: @B=0.01 @B=0.10 @B=0.30 + : ------------------------------------------------------------------------------------------------------------------- + : dataset gBDT_D2_S01 : 0.161 (0.144) 0.517 (0.520) 0.820 (0.819) + : dataset gBDT_D2_C300 : 0.148 (0.149) 0.529 (0.531) 0.809 (0.811) + : dataset gBDT_D2 : 0.154 (0.153) 0.530 (0.533) 0.816 (0.817) + : dataset gBDT_D3 : 0.142 (0.159) 0.543 (0.547) 0.804 (0.808) + : dataset gBDT_D4 : 0.135 (0.153) 0.538 (0.545) 0.800 (0.806) + : dataset gBDT_D5 : 0.135 (0.168) 0.514 (0.544) 0.790 (0.798) + : dataset gBDT_D6 : 0.134 (0.178) 0.517 (0.548) 0.798 (0.809) + : ------------------------------------------------------------------------------------------------------------------- + : +Dataset:dataset : Created tree 'TestTree' with 17381 events + : +Dataset:dataset : Created tree 'TrainTree' with 17381 events + : +Factory : Thank you for using TMVA! + : For citation information, please visit: http://tmva.sf.net/citeTMVA.html +(myenv) [dshekar@lxplus968 bdt_train]$ Connection to lxplus.cern.ch closed by remote host. +Connection to lxplus.cern.ch closed. +client_loop: send disconnect: Broken pipe +danush@danush-5 ZpTreweighting % + [Restored May 13, 2026 at 08:46:47] +Last login: Wed May 13 08:46:33 on console +Restored session: Wed May 13 08:44:32 CDT 2026 +% danush@danush-5 ZpTreweighting % kinit dshekar@FNL.GOV +dshekar@FNL.GOV's password: +kinit: krb5_get_init_creds: unable to reach any KDC in realm FNL.GOV, tried 0 KDCs +danush@danush-5 ZpTreweighting % kinit dshekar@FNAL.GOV +dshekar@FNAL.GOV's password: +danush@danush-5 ZpTreweighting % ssh -Y dshekar@midway3.rcc.uchicago.edu +(dshekar@midway3.rcc.uchicago.edu) Password: +(dshekar@midway3.rcc.uchicago.edu) Password: +(dshekar@midway3.rcc.uchicago.edu) Password: +dshekar@midway3.rcc.uchicago.edu's password: +^C +danush@danush-5 ZpTreweighting % ssh -Y dshekar@midway3.rcc.uchicago.edu +(dshekar@midway3.rcc.uchicago.edu) Password: +(dshekar@midway3.rcc.uchicago.edu) Password: + +danush@danush-5 ZpTreweighting % ssh -Y dshekar@midway3.rcc.uchicago.edu +(dshekar@midway3.rcc.uchicago.edu) Password: +(dshekar@midway3.rcc.uchicago.edu) Password: + +danush@danush-5 ZpTreweighting % lxplus +(dshekar@lxplus.cern.ch) Password: +(dshekar@lxplus.cern.ch) Your 2nd factor (dshekar): 809564 +* ******************************************************************** +* Welcome to lxplus966.cern.ch, Red Hat Enterprise Linux release 9.7 (Plow) +* Archive of news is available in /etc/motd-archive +* Reminder: you have agreed to the CERN +* computing rules, in particular OC5. CERN implements +* the measures necessary to ensure compliance. +* https://cern.ch/ComputingRules +* Puppet environment: qa, Roger state: production +* Foreman hostgroup: lxplus/nodes/login +* Availability zone: cern-geneva-b +* LXPLUS Public Login Service - http://lxplusdoc.web.cern.ch/ +* Please read LXPLUS Privacy Notice in http://cern.ch/go/TpV7 +* ******************************************************************** +* /!\ LxPlus now requires 2FA from outside of CERN /!\ +* All details for 2FA enforcing on https://cern.ch/otg0156449 + +Last login: Tue May 12 14:39:17 2026 from 192.190.216.122 +[dshekar@lxplus966 ~]$ ls +__MACOSX private public +[dshekar@lxplus966 ~]$ cd public/RDF/ +Latinos/ mkShapesRDF/ mkShapesRDF_postproc/ mkShapesRDF_privPostProc/ PlotsConfigurationsRun3/ PlotsConfigurationsRun3_fork/ +[dshekar@lxplus966 ~]$ cd public/RDF/mkShapesRDF_privPostProc/ +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ ls /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22 +Summer22_130x_nAODv12_Full2022v12/ Summer22EE_130x_nAODv12_Full2022v12/ +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ ls /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22_130x_nAODv12_Full2022v12/ +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_FlavorQCDup_suffix +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jerdo_suffix MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_HF_2022do_suffix +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jerup_suffix MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_HF_2022up_suffix +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_Absolute_2022do_suffix MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_HFdo_suffix +MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_Absolute_2022up_suffix MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight__jesRegroed_HFup_suffix 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mkShapesRDF_privPostProc]$ ls +Dockerfile environment.yml install.sh mkShapesRDF myenv pyproject.toml setup.cfg tests WZ_slc7_amd64_gcc10_CMSSW_12_4_8_WZto3LNu_powheg-pythia8.tgz +docs examples MANIFEST.in mkShapesRDF.egg-info powheg.input README.md start.sh utils +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/Sites_cfg.py +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/ +Run2018_UL2018_nAODv9.py Run2024_ReRecoCDE_PromptFGHI_nAODv15_EGamma.py Run2025_PromptCDEFG_nAODv15_MuonEG.py Summer20UL18_106x_nAODv9.py Summer23BPix_130x_nAODv12.py +Run2022EE_Prompt_nAODv12.py Run2024_ReRecoCDE_PromptFGHI_nAODv15_MuonEG.py Run2025_PromptCDEFG_nAODv15_Muon.py Summer22_130x_nAODv12.py Summer24_150x_nAODv15.py +Run2022_ReReco_nAODv12.py Run2024_ReRecoCDE_PromptFGHI_nAODv15_Muon.py Run2025_PromptCDEFG_nAODv15.py Summer22EE_130x_nAODv12.py +Run2023BPix_Prompt_nAODv12.py Run2024_ReRecoCDE_PromptFGHI_nAODv15.py samplesCrossSections_13p6TeV.py Summer22EE_130x_nAODv12_testrecipes.py +Run2023_Prompt_nAODv12.py Run2025_PromptCDEFG_nAODv15_EGamma.py samplesCrossSections_UL.py Summer23_130x_nAODv12.py +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer22EE_130x_nAODv12.py +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ ls /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/ +ls: cannot access '/eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022v12__MCCorr2022v12JetScaling__l2tight/': No such file or directory +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ ls /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/ +nanoLatino_ZH_Zto2L_Hto2WtoLNu2Q__part0.root nanoLatino_ZH_Zto2L_Hto2WtoLNu2Q__part19.root nanoLatino_ZH_Zto2L_Hto2WtoLNu2Q__part28.root 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+mkShapesRDF/processor/condor/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/ZH_Zto2L_Hto2WtoLNu2Q__part45/script.py +mkShapesRDF/processor/condor/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/ZH_Zto2L_Hto2WtoLNu2Q__part45/out.txt +mkShapesRDF/processor/condor/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/ZH_Zto2L_Hto2WtoLNu2Q__part45/err.txt +mkShapesRDF/processor/condor/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/submit.jdl +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer22EE_130x_nAODv12.py +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/samplesCrossSections_13p6TeV.py +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ vim mkShapesRDF/processor/framework/samples/Summer22EE_130x_nAODv12.py +[dshekar@lxplus966 mkShapesRDF_privPostProc]$ source start.sh +first source of start.sh +(myenv) [dshekar@lxplus966 mkShapesRDF_privPostProc]$ rm realequivregions-*^C +(myenv) [dshekar@lxplus966 mkShapesRDF_privPostProc]$ mkPostProc -o 0 -p Summer22EE_130x_nAODv12_Full2022v12 -s MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight -T GluGluZH_Zto2L_Hto2WtoLNu2Q + xxx +cp: cannot stat 'xxx': No such file or directory +['Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set', 'In order to run please obtain valid proxy via "voms-proxy-init -voms cms -rfc"', 'and setup X509_USER_PROXY or setup X509_USER_KEY/X509_USER_CERT in your environment'] 3 +root://cms-xrd-global.cern.ch/Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set + + +Try with chunksize 3 +Iteration: 0 +Error in : [ERROR] Server responded with an error: [3010] Opening relative path 'Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set' is disallowed. + +Error in : [ERROR] Server responded with an error: [3010] Opening relative path 'Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set' is disallowed. + +Traceback (most recent call last): + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/myenv/bin/mkPostProc", line 7, in + sys.exit(main()) + ~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/scripts/mkPostProc.py", line 225, in main + a.run() + ~~~~~^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/framework/processor.py", line 477, in run + genEventSumw += computeSumW(files, len(files)) + ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^ + File "/afs/cern.ch/user/d/dshekar/public/RDF/mkShapesRDF_privPostProc/mkShapesRDF/processor/framework/processor.py", line 457, in computeSumW + df = ROOT.RDataFrame("Runs", files_tmp) + File "/cvmfs/sft.cern.ch/lcg/views/LCG_109/x86_64-el9-gcc13-opt/lib/ROOT/_pythonization/_rdf_namespace.py", line 103, in rdataframe + rdf = local_rdf(*args, **kwargs) +TypeError: none of the 10 overloaded methods succeeded. Full details: + RDataFrame::RDataFrame(string_view treename, initializer_list filenames, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + invalid_argument: RDataFrame: could not open file "root://cms-xrd-global.cern.ch/Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set". + RDataFrame::RDataFrame(ROOT::RDataFrame&&) => + TypeError: takes at most 1 arguments (2 given) + RDataFrame::RDataFrame(string_view treeName, TDirectory* dirPtr, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + TypeError: could not convert argument 2 + RDataFrame::RDataFrame(TTree& tree, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + TypeError: could not convert argument 1 + RDataFrame::RDataFrame(const ROOT::RDataFrame&) => + TypeError: takes at most 1 arguments (2 given) + RDataFrame::RDataFrame(string_view treename, const vector& filenames, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + invalid_argument: RDataFrame: could not open file "root://cms-xrd-global.cern.ch/Neither X509_USER_PROXY or X509_USER_KEY/X509_USER_CERT are set". + bad argument type for built-in operation + RDataFrame::RDataFrame(ULong64_t numEntries) => + TypeError: takes at most 1 arguments (2 given) + RDataFrame::RDataFrame(unique_ptr, const ROOT::RDataFrame::ColumnNames_t& defaultColumns = {}) => + TypeError: could not convert argument 1 + RDataFrame::RDataFrame(ROOT::RDF::Experimental::RDatasetSpec spec) => + TypeError: takes at most 1 arguments (2 given) +(myenv) [dshekar@lxplus966 mkShapesRDF_privPostProc]$ voms-proxy-init --voms cms -valid 192:0 +Enter GRID pass phrase: +Your identity: /DC=ch/DC=cern/OU=Organic Units/OU=Users/CN=dshekar/CN=862901/CN=Danush Shekar +Contacting voms-cms-auth.cern.ch:443 [/DC=ch/DC=cern/OU=computers/CN=cms-auth.cern.ch] "cms"... Done + +Created proxy in /tmp/x509up_u154072. + +Your proxy is valid until Tue May 26 23:38:06 2026 CEST + +(myenv) [dshekar@lxplus966 mkShapesRDF_privPostProc]$ mkPostProc -o 0 -p Summer22EE_130x_nAODv12_Full2022v12 -s MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight -T GluGluZH_Zto2L_Hto2WtoLNu2Q +/tmp/x509up_u154072 +['/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/9ea158ec-bcc3-4f56-87f8-7070549cba45.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/b53999d2-c89d-4c4e-923e-a62abe9215c0.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/f43e6f98-b474-4c3f-accb-e9f4bd7a0dc6.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/8902ff81-f1f1-4137-813f-058162fac30b.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/f0b7e6b2-73dc-4b52-aad4-298b07ac13d8.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/0aa8e921-1011-4dc2-b209-698ca78abdf1.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/c5d94caf-10e9-4d7b-a3d1-cc26005e0451.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/87e9b7b7-9fc8-44c1-8d0f-9e8325e004f0.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/848fe5fd-330f-4516-a9fb-a2b8447881cf.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/d30dae66-5563-45f8-bc39-487b82bca741.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/463fcc13-be6c-4f9a-b4cc-2acff2869f8c.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/a7f5cc88-78ca-4aea-93f6-8718e36e8370.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/5814f54a-e3d5-4827-99ea-2c6adc111f9d.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/67b99c53-2515-411c-9096-f7955aa19bcc.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/cd9c9bd2-40f8-43d8-9867-332ffb2b8349.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/d5c71409-27c6-4dc9-af68-d80ec8dd229c.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/3e455f61-0ed4-45cd-8be2-e6073977cd46.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/3780aad8-5444-49cf-a31a-26694325dab3.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/4dfb880d-e5c9-4cb6-8b69-0e7d8526acae.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/7838110e-f446-4de9-86da-09ab60c0211e.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/3363ab79-90eb-4805-b61f-099aa9c3496a.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/5bff0387-d69f-4200-aada-aee0ffb23575.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/d8816d59-84f7-46e6-8c2e-9fa8f231f2a4.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/230c3576-3320-4b41-8d20-9a66b1780117.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/692a2570-2e6c-4a70-9646-859e9d756354.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/9996a314-560e-480e-b622-0ed8d66c2535.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/87c2b5cf-af2f-473a-8fc9-dd07c814a54e.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/fc574a93-667c-4293-b5a6-96702731260c.root', '/store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/5da95ec3-8eec-4b23-ae85-ad1e8b9e0621.root'] 29 +root://cms-xrd-global.cern.ch//store/mc/Run3Summer22EENanoAODv12/GluGluZH_Zto2L_Hto2WtoLNu2Q_M-125_TuneCP5_13p6TeV_powheg-jhugen-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_postEE_v6-v2/2560000/9ea158ec-bcc3-4f56-87f8-7070549cba45.root + + +Try with chunksize 29 +Iteration: 0 +Warning in : no dictionary for class edm::Hash<1> is available +Warning in : no dictionary for class edm::ProcessHistory is available +Warning in : no dictionary for class edm::ProcessConfiguration is available +Warning in : no dictionary for class edm::ParameterSetBlob is available +Warning in : no dictionary for class pair,edm::ParameterSetBlob> is available +Finally computed sumW: 68064.0678 +Submitting job(s)............................. +29 job(s) submitted to cluster 15644954. +(myenv) [dshekar@lxplus966 mkShapesRDF_privPostProc]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/18/26 23:49:46 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS +dshekar ID: 15644954 5/18 23:38 _ 29 _ 29 15644954.0-28 + +Total for query: 29 jobs; 0 completed, 0 removed, 0 idle, 29 running, 0 held, 0 suspended +Total for dshekar: 29 jobs; 0 completed, 0 removed, 0 idle, 29 running, 0 held, 0 suspended +Total for all users: 14765 jobs; 6859 completed, 493 removed, 6114 idle, 1264 running, 35 held, 0 suspended + +(myenv) [dshekar@lxplus966 mkShapesRDF_privPostProc]$ Connection to lxplus.cern.ch closed by remote host. +Connection to lxplus.cern.ch closed. +client_loop: send disconnect: Broken pipe +danush@danush-5 ZpTreweighting % +danush@danush-5 ZpTreweighting % +danush@danush-5 ZpTreweighting % lxplus +(dshekar@lxplus.cern.ch) Password: +(dshekar@lxplus.cern.ch) Your 2nd factor (dshekar): 130308 +* ******************************************************************** +* Welcome to lxplus976.cern.ch, Red Hat Enterprise Linux release 9.7 (Plow) +* Archive of news is available in /etc/motd-archive +* Reminder: you have agreed to the CERN +* computing rules, in particular OC5. CERN implements +* the measures necessary to ensure compliance. +* https://cern.ch/ComputingRules +* Puppet environment: qa, Roger state: production +* Foreman hostgroup: lxplus/nodes/login +* Availability zone: cern-geneva-b +* LXPLUS Public Login Service - http://lxplusdoc.web.cern.ch/ +* Please read LXPLUS Privacy Notice in http://cern.ch/go/TpV7 +* ******************************************************************** +* /!\ LxPlus now requires 2FA from outside of CERN /!\ +* All details for 2FA enforcing on https://cern.ch/otg0156449 + +[dshekar@lxplus976 ~]$ +[dshekar@lxplus976 ~]$ +[dshekar@lxplus976 ~]$ condor_q + + +-- Schedd: bigbird22.cern.ch : <188.185.121.235:9618?... @ 05/19/26 05:32:48 +OWNER BATCH_NAME SUBMITTED DONE RUN IDLE TOTAL JOB_IDS +dshekar ID: 15644954 5/18 23:38 24 5 _ 29 15644954.15-28 + +Total for query: 5 jobs; 0 completed, 0 removed, 0 idle, 5 running, 0 held, 0 suspended +Total for dshekar: 5 jobs; 0 completed, 0 removed, 0 idle, 5 running, 0 held, 0 suspended +Total for all users: 13897 jobs; 6859 completed, 493 removed, 5716 idle, 806 running, 23 held, 0 suspended + +[dshekar@lxplus976 ~]$ root -l /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/nanoLatino_GluGluZH_Zto2L_Hto2WtoLNu2Q__part0.root +root [0] +Attaching file /eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/nanoLatino_GluGluZH_Zto2L_Hto2WtoLNu2Q__part0.root as _file0... +Warning in : no dictionary for class edm::Hash<1> is available +Warning in : no dictionary for class edm::ProcessHistory is available +Warning in : no dictionary for class edm::ProcessConfiguration is available +Warning in : no dictionary for class edm::ParameterSetBlob is available +Warning in : no dictionary for class pair,edm::ParameterSetBlob> is available +(TFile *) 0x56139c8bd670 +root [1] .ls +TNetXNGFile** root://eoshome-d.cern.ch//eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/nanoLatino_GluGluZH_Zto2L_Hto2WtoLNu2Q__part0.root + TNetXNGFile* root://eoshome-d.cern.ch//eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/nanoLatino_GluGluZH_Zto2L_Hto2WtoLNu2Q__part0.root + KEY: TTree Events;1 + KEY: TTree Runs;1 Runs + KEY: TTree ParameterSets;1 Parameter sets + KEY: TTree LuminosityBlocks;1 LuminosityBlocks + KEY: TTree MetaData;1 Job metadata + KEY: TObjString untagged;1 Collectable string class +root [2] Events->Print() +****************************************************************************** +*Tree :Events : * +*Entries : 7312 : Total = 124027082 bytes File Size = 27937867 * +* : : Tree compression factor = 4.64 * +****************************************************************************** +*Br 0 :nCleanJet : nCleanJet/I * +*Entries : 7312 : Total Size= 29827 bytes File Size = 3431 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 8.55 * +*............................................................................* +*Br 1 :CleanJet_correctedjetIdx : CleanJet_correctedjetIdx[nCleanJet]/l * +*Entries : 7312 : Total Size= 249481 bytes File Size = 16050 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 15.50 * +*............................................................................* +*Br 2 :CleanJet_eta : CleanJet_eta[nCleanJet]/F * +*Entries : 7312 : Total Size= 139685 bytes File Size = 68066 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 2.04 * +*............................................................................* +*Br 3 :CleanJet_jetIdx : CleanJet_jetIdx[nCleanJet]/l * +*Entries : 7312 : Total Size= 249436 bytes File Size = 16821 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 14.79 * +*............................................................................* +*Br 4 :CleanJet_mass : CleanJet_mass[nCleanJet]/F * +*Entries : 7312 : Total Size= 139690 bytes File Size = 104079 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 1.34 * +*............................................................................* +*Br 5 :CleanJet_phi : CleanJet_phi[nCleanJet]/F * +*Entries : 7312 : Total Size= 139685 bytes File Size = 66450 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 2.09 * +*............................................................................* +*Br 6 :CleanJet_pt : CleanJet_pt[nCleanJet]/F * +*Entries : 7312 : Total Size= 139680 bytes File Size = 102645 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 1.35 * +*............................................................................* +*Br 7 :nJet : nJet/I * +*Entries : 7312 : Total Size= 29802 bytes File Size = 3494 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 8.39 * +*............................................................................* +*Br 8 :Jet_btagPNetCvB : Jet_btagPNetCvB[nJet]/F * +*Entries : 7312 : Total Size= 208496 bytes File Size = 65281 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 3.18 * +*............................................................................* +*Br 9 :Jet_btagPNetCvL : Jet_btagPNetCvL[nJet]/F * +*Entries : 7312 : Total Size= 208496 bytes File Size = 75409 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 2.76 * +*............................................................................* +*Br 10 :Jet_chHEF : Jet_chHEF[nJet]/F * +*Entries : 7312 : Total Size= 208466 bytes File Size = 58767 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 3.54 * +*............................................................................* +*Br 11 :Jet_electronIdx1 : Jet_electronIdx1[nJet]/S * +*Entries : 7312 : Total Size= 119225 bytes File Size = 14034 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 8.45 * +*............................................................................* +*Br 12 :Jet_mass : Jet_mass[nJet]/F * +*Entries : 7312 : Total Size= 208461 bytes File Size = 82890 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 2.51 * +*............................................................................* +*Br 13 :Jet_btagDeepFlavCvB : Jet_btagDeepFlavCvB[nJet]/F * +*Entries : 7312 : Total Size= 208516 bytes File Size = 68657 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 3.03 * +*............................................................................* +*Br 14 :Jet_genJetIdx : Jet_genJetIdx[nJet]/S * +*Entries : 7312 : Total Size= 119210 bytes File Size = 19595 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 6.05 * +*............................................................................* +*Br 15 :Jet_electronIdx2 : Jet_electronIdx2[nJet]/S * +*Entries : 7312 : Total Size= 119225 bytes File Size = 7206 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 16.46 * +*............................................................................* +*Br 16 :Jet_muEF : Jet_muEF[nJet]/F * +*Entries : 7312 : Total Size= 208461 bytes File Size = 21850 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 9.51 * +*............................................................................* +*Br 17 :Jet_eta : Jet_eta[nJet]/F * +*Entries : 7312 : Total Size= 208456 bytes File Size = 105133 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 1.98 * +*............................................................................* +*Br 18 :Jet_btagDeepFlavQG : Jet_btagDeepFlavQG[nJet]/F * +*Entries : 7312 : Total Size= 208511 bytes File Size = 73300 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 2.84 * +*............................................................................* +*Br 19 :Jet_jetId : Jet_jetId[nJet]/I * +*Entries : 7312 : Total Size= 208459 bytes File Size = 15807 * +*Baskets : 1 : 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+*............................................................................* +*Br 1882 :LepSF3l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67__Down : * +* | LepSF3l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67__Down/F * +*Entries : 7312 : Total Size= 30052 bytes File Size = 6736 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 4.36 * +*............................................................................* +*Br 1883 :LepSF3l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_POG : * +* | LepSF3l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_POG/F * +*Entries : 7312 : Total Size= 30092 bytes File Size = 8912 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 3.30 * +*............................................................................* +*Br 1884 :LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_Tight_HWW : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_Tight_HWW/F* +*Entries : 7312 : Total Size= 30097 bytes File Size = 9437 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 3.11 * +*............................................................................* +*Br 1885 :LepSF3l__ele_wp90iso__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67 : * +* | LepSF3l__ele_wp90iso__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67/F * +*Entries : 7312 : Total Size= 30087 bytes File Size = 9299 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 3.16 * +*............................................................................* +*Br 1886 :LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__Down : * +* | LepSF3l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__Down/F * +*Entries : 7312 : Total Size= 30042 bytes File Size = 6046 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 4.86 * +*............................................................................* +*Br 1887 :LepSF4l__ele_wp90iso__mu_cut_TightID_POG : * +* | LepSF4l__ele_wp90iso__mu_cut_TightID_POG/F * +*Entries : 7312 : Total Size= 29982 bytes File Size = 350 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 83.88 * +*............................................................................* +*Br 1888 :LepSF4l__ele_testrecipes__Up : LepSF4l__ele_testrecipes__Up/F * +*Entries : 7312 : Total Size= 29922 bytes File Size = 346 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 84.81 * +*............................................................................* +*Br 1889 :LepSF4l__mu_cut_Tight_HWW__Down : LepSF4l__mu_cut_Tight_HWW__Down/F* +*Entries : 7312 : Total Size= 29937 bytes File Size = 345 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 85.07 * +*............................................................................* +*Br 1890 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 7312 : Total Size= 30217 bytes File Size = 405 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 72.60 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: 1 : Basket Size= 32000 bytes Compression= 74.79 * +*............................................................................* +*Br 1894 :LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__Down : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__Down/F * +*Entries : 7312 : Total Size= 30017 bytes File Size = 365 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 80.45 * +*............................................................................* +*Br 1895 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F* +*Entries : 7312 : Total Size= 30217 bytes File Size = 409 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 71.89 * +*............................................................................* +*Br 1896 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_POG : * +* | 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:LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 7312 : Total Size= 30222 bytes File Size = 406 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 72.43 * +*............................................................................* +*Br 1900 :LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67 : * +* | LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67/F* +*Entries : 7312 : Total Size= 30107 bytes File Size = 383 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 76.72 * +*............................................................................* +*Br 1901 :LepSF4l__ele_testrecipes__mu_cut_TightID_POG : * +* | LepSF4l__ele_testrecipes__mu_cut_TightID_POG/F * +*Entries : 7312 : Total Size= 30002 bytes File Size = 354 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 82.94 * +*............................................................................* +*Br 1902 :LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 7312 : Total Size= 30202 bytes File Size = 402 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 73.14 * +*............................................................................* +*Br 1903 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__Up : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__Up/F * +*Entries : 7312 : Total Size= 30027 bytes File Size = 367 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 80.02 * +*............................................................................* +*Br 1904 :LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW : * +* | LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW/F* +*Entries : 7312 : Total Size= 30112 bytes File Size = 384 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 76.52 * +*............................................................................* +*Br 1905 :LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F* +*Entries : 7312 : Total Size= 30157 bytes File Size = 393 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 74.79 * +*............................................................................* +*Br 1906 :LepSF4l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW__Down : * +* | LepSF4l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_HWW__Down/F * +*Entries : 7312 : Total Size= 30057 bytes File Size = 365 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 80.47 * +*............................................................................* +*Br 1907 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67 : * +* | 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bytes File Size = 381 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 77.11 * +*............................................................................* +*Br 1914 :LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__mu_cut_Tight_HWW : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__mu_cut_Tight_HWW/F * +*Entries : 7312 : Total Size= 30077 bytes File Size = 377 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 77.92 * +*............................................................................* +*Br 1915 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__Down : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__Down/F * +*Entries : 7312 : Total Size= 30037 bytes File Size = 373 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 78.73 * +*............................................................................* +*Br 1916 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__Up : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_Run3__Up/F * +*Entries : 7312 : Total Size= 30032 bytes File Size = 364 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 80.68 * +*............................................................................* +*Br 1917 :LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__Down : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__Down/F * +*Entries : 7312 : Total Size= 30022 bytes File Size = 366 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 80.23 * +*............................................................................* +*Br 1918 :LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_POG : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90__mu_cut_TightID_POG/F * +*Entries : 7312 : Total Size= 30052 bytes File Size = 368 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 79.81 * +*............................................................................* +*Br 1919 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67 : * +* | 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78.53 * +*............................................................................* +*Br 1931 :LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_POG : * +* | LepSF4l__ele_mvaWinter22V2Iso_WP90_tthMVA_HWW__mu_cut_TightID_POG/F* +*Entries : 7312 : Total Size= 30107 bytes File Size = 375 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 78.35 * +*............................................................................* +*Br 1932 :LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_POG : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_Run3__mu_cut_TightID_POG/F * +*Entries : 7312 : Total Size= 30092 bytes File Size = 372 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 78.98 * +*............................................................................* +*Br 1933 :LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoTight_HWW_tthmva_67 : * +* | LepSF4l__ele_testrecipes__mu_cut_TightID_pfIsoTight_HWW_tthmva_67/F* +*Entries : 7312 : Total Size= 30107 bytes File Size = 383 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 76.72 * +*............................................................................* +*Br 1934 :LepSF4l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67__Up : * +* | LepSF4l__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67__Up/F * +*Entries : 7312 : Total Size= 30042 bytes File Size = 366 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 80.24 * +*............................................................................* +*Br 1935 :LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67 : * +* | LepSF4l__ele_cutBased_MediumID_tthMVA_HWW__mu_cut_TightID_pfIsoLoose_HWW_tthmva_67/F* +*Entries : 7312 : Total Size= 30192 bytes File Size = 400 * +*Baskets : 1 : Basket Size= 32000 bytes Compression= 73.50 * +*............................................................................* +*Br 1936 :LepSF4l__ele_testrecipes__Down : LepSF4l__ele_testrecipes__Down/F * +*Entries : 7312 : Total Size= 29932 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+*............................................................................* \ No newline at end of file diff --git a/truth_plots_zh3l/plotting.cpp b/truth_plots_zh3l/plotting.cpp new file mode 100644 index 00000000..f4b31541 --- /dev/null +++ b/truth_plots_zh3l/plotting.cpp @@ -0,0 +1,322 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include "TFile.h" +#include "TH1D.h" +#include "TCanvas.h" +#include "TLegend.h" +#include "TLatex.h" +#include "TStyle.h" +#include "TSystem.h" +#include "TGraphAsymmErrors.h" +// Many modern ROOT environments bundle this. If not, download json.hpp +#include + +using json = nlohmann::json; + +const char* qqZH_refinedWs = "/eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/GluGluZH_Zto2L_Hto2WtoLNu2Q_results/reco_plots_refinedWHadReco-cutFlow.root"; +const char* ggZH_refinedWs = "/eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/ZH_Zto2L_Hto2WtoLNu2Q_results/reco_plots_refinedWHadReco-cutFlow.root"; +const char* WZ_refinedWs = "/eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/WZ_results/reco_plots_refinedWHadReco-cutFlow.root"; + +const char* qqZH_normalWs = "/eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/GluGluZH_Zto2L_Hto2WtoLNu2Q_results/reco_plots_normalWHadReco-cutFlow.root"; +const char* ggZH_normalWs = "/eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/ZH_Zto2L_Hto2WtoLNu2Q_results/reco_plots_normalWHadReco-cutFlow.root"; +const char* WZ_normalWs = "/eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/WZ_results/reco_plots_normalWHadReco-cutFlow.root"; + +void setHistStyle(TH1D* h, int color, int style) +{ + h->SetDirectory(nullptr); + h->SetStats(0); + h->SetLineColor(color); + h->SetLineStyle(style); + h->SetLineWidth(2); +} + +TH1D* getHist(TFile* f, const char* name, const char* cloneName) +{ + TH1D* h = (TH1D*)f->Get(name); + if (!h) { + std::cerr << "Missing histogram: " << name << " in file " << f->GetName() << std::endl; + return nullptr; + } + TH1D* hc = (TH1D*)h->Clone(cloneName); + hc->SetDirectory(nullptr); + return hc; +} + +void plotOne( + TFile* f_qq_ref, + TFile* f_gg_ref, + TFile* f_wz_ref, + TFile* f_qq_norm, + TFile* f_gg_norm, + TFile* f_wz_norm, + const char* hname +) +{ + TH1D* h_qq_ref = getHist(f_qq_ref, hname, "h_qq_ref"); + TH1D* h_gg_ref = getHist(f_gg_ref, hname, "h_gg_ref"); + TH1D* h_wz_ref = getHist(f_wz_ref, hname, "h_wz_ref"); + TH1D* h_qq_norm = getHist(f_qq_norm, hname, "h_qq_norm"); + TH1D* h_gg_norm = getHist(f_gg_norm, hname, "h_gg_norm"); + TH1D* h_wz_norm = getHist(f_wz_norm, hname, "h_wz_norm"); + + if (!h_qq_ref || !h_gg_ref || !h_qq_norm || !h_gg_norm || !h_wz_norm || !h_wz_ref) return; + TH1D* h_qq_ref_plot = (TH1D*)h_qq_ref->Clone("h_qq_ref_plot"); + TH1D* h_gg_ref_plot = (TH1D*)h_gg_ref->Clone("h_gg_ref_plot"); + TH1D* h_wz_ref_plot = (TH1D*)h_wz_ref->Clone("h_wz_ref_plot"); + TH1D* h_qq_norm_plot = (TH1D*)h_qq_norm->Clone("h_qq_norm_plot"); + TH1D* h_gg_norm_plot = (TH1D*)h_gg_norm->Clone("h_gg_norm_plot"); + TH1D* h_wz_norm_plot = (TH1D*)h_wz_norm->Clone("h_wz_norm_plot"); + + auto norm = [](TH1D* h) { + double integral = h->Integral(); + if (integral > 0) h->Scale(1.0 / integral); + else std::cerr << "Warning: Histogram " << h->GetName() << " has zero integral, cannot normalize." << std::endl; + }; + + norm(h_qq_ref_plot); + norm(h_gg_ref_plot); + norm(h_wz_ref_plot); + norm(h_qq_norm_plot); + norm(h_gg_norm_plot); + norm(h_wz_norm_plot); + + setHistStyle(h_qq_ref_plot, kRed+1, 1); + setHistStyle(h_gg_ref_plot, kBlue+1, 1); + setHistStyle(h_wz_ref_plot, kGreen+2, 1); + setHistStyle(h_qq_norm_plot, kRed+1, 2); + setHistStyle(h_gg_norm_plot, kBlue+1, 2); + setHistStyle(h_wz_norm_plot, kGreen+2, 2); + + double ymax = std::max({ + h_qq_ref_plot->GetMaximum(), + h_gg_ref_plot->GetMaximum(), + h_wz_ref_plot->GetMaximum(), + h_qq_norm_plot->GetMaximum(), + h_gg_norm_plot->GetMaximum(), + h_wz_norm_plot->GetMaximum() + }); + + TCanvas* c = new TCanvas(Form("c_%s", hname), hname, 800, 700); + + c->SetTopMargin(0.08); + c->SetRightMargin(0.05); + c->SetLeftMargin(0.12); + c->SetBottomMargin(0.12); + + h_qq_ref_plot->SetMaximum(1.25 * ymax); + h_qq_ref_plot->SetMinimum(0.0); + h_qq_ref_plot->SetTitle(Form("%s;Bins;Normalized events", hname)); + + // h_qq_ref_plot->SetStats(0); + // h_gg_ref_plot->SetStats(0); + // h_wz_ref_plot->SetStats(0); + // h_qq_norm_plot->SetStats(0); + // h_gg_norm_plot->SetStats(0); + // h_wz_norm_plot->SetStats(0); + h_qq_ref_plot->Draw("hist"); + h_gg_ref_plot->Draw("hist same"); + h_wz_ref_plot->Draw("hist same"); + h_qq_norm_plot->Draw("hist same"); + h_gg_norm_plot->Draw("hist same"); + h_wz_norm_plot->Draw("hist same"); + + TLegend* leg = new TLegend(0.55, 0.65, 0.88, 0.88); + leg->SetBorderSize(0); + leg->SetFillStyle(0); + leg->AddEntry(h_qq_ref_plot, Form("qqZH (refined, N=%.2f)", h_qq_ref->Integral()), "l"); + leg->AddEntry(h_gg_ref_plot, Form("GluGluZH (refined, N=%.2f)", h_gg_ref->Integral()), "l"); + leg->AddEntry(h_wz_ref_plot, Form("WZ (refined, N=%.2f)", h_wz_ref->Integral()), "l"); + leg->AddEntry(h_qq_norm_plot, Form("qqZH (normal, N=%.2f)", h_qq_norm->Integral()), "l"); + leg->AddEntry(h_gg_norm_plot, Form("GluGluZH (normal, N=%.2f)", h_gg_norm->Integral()), "l"); + leg->AddEntry(h_wz_norm_plot, Form("WZ (normal, N=%.2f)", h_wz_norm->Integral()), "l"); + + leg->Draw(); + + c->SaveAs(Form("plots/%s.png", hname)); + delete c; +} + +std::vector readCutflow(const std::string& path, const std::string& sample) +{ + std::vector vals(7, 0.0); + std::ifstream in(path); + if (!in.is_open()) { + std::cerr << "Cannot open " << path << std::endl; + return vals; + } + + json j; + try { + in >> j; + } catch (...) { + std::cerr << "Error parsing JSON " << path << std::endl; + return vals; + } + + if (!j.contains(sample) || !j[sample].is_array()) { + std::cerr << "Missing or invalid sample key: " << sample << std::endl; + return vals; + } + + auto arr = j[sample]; + int n = std::min(7, arr.size()); + for (int i = 0; i < n; ++i) { + vals[i] = arr[i].get(); + } + return vals; +} + +void plotCutflowComparison() +{ + gStyle->SetOptStat(0); + gSystem->mkdir("plots", kTRUE); + + std::vector labels = { + "All", + "Lepton pre-sel", + "mll veto", + "Z window", + "Zg veto", + "Jet pre-sel", + "Signal region" + }; + + const char* refinedJson = "/eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/WZ_results/recoPlot_refined_cutFlow.json"; + // cutflow in WZ folder contains results from all three samples as it was the last sample to be analyzed. + + auto c1 = readCutflow(refinedJson, "ZH_Zto2L_Hto2WtoLNu2Q"); + auto c2 = readCutflow(refinedJson, "GluGluZH_Zto2L_Hto2WtoLNu2Q"); + auto c3 = readCutflow(refinedJson, "WZ"); + + auto normalize = [](std::vector& v) { + if (!v.empty() && v[0] != 0.0) { + double n0 = v[0]; + for (auto& x : v) x /= n0; + } + }; + + normalize(c1); + normalize(c2); + normalize(c3); + + TGraph* g1 = new TGraph(7); + TGraph* g2 = new TGraph(7); + TGraph* g3 = new TGraph(7); + + for (int i = 0; i < 7; ++i) { + g1->SetPoint(i, i, c1[i]); + g2->SetPoint(i, i, c2[i]); + g3->SetPoint(i, i, c3[i]); + } + + g1->SetLineColor(kRed+1); + g1->SetMarkerColor(kRed+1); + g1->SetMarkerStyle(20); + g1->SetMarkerSize(1.1); + g1->SetLineWidth(2); + + g2->SetLineColor(kBlue+1); + g2->SetMarkerColor(kBlue+1); + g2->SetMarkerStyle(21); + g2->SetMarkerSize(1.1); + g2->SetLineWidth(2); + + g3->SetLineColor(kGreen+2); + g3->SetMarkerColor(kGreen+2); + g3->SetMarkerStyle(22); + g3->SetMarkerSize(1.1); + g3->SetLineWidth(2); + + TCanvas* c = new TCanvas("c_cutflow", "cutflow", 1000, 700); + c->SetBottomMargin(0.28); + c->SetLeftMargin(0.12); + c->SetGridx(); + c->SetGridy(); + + TH1F* frame = c->DrawFrame(-0.5, 0.0, 6.5, 1.2); + frame->SetTitle("Refined cutflow normalized to first bin; ;Normalized events"); + + frame->GetXaxis()->SetLabelSize(0); + frame->GetXaxis()->SetTickSize(0); + + g1->Draw("PL SAME"); + g2->Draw("PL SAME"); + g3->Draw("PL SAME"); + + for (int i = 0; i < 7; ++i) { + TLatex* t = new TLatex(i, -0.08, labels[i].c_str()); + t->SetTextAlign(23); + t->SetTextSize(0.03); + t->SetTextAngle(35); + t->Draw(); + } + + TLegend* leg = new TLegend(0.55, 0.70, 0.88, 0.88); + leg->SetBorderSize(0); + leg->SetFillStyle(0); + leg->AddEntry(g1, "GluGluZH", "lp"); + leg->AddEntry(g2, "qqZH", "lp"); + leg->AddEntry(g3, "WZ", "lp"); + leg->Draw(); + + c->SaveAs("plots/cutflow_comparison.png"); +} + +void plotting() +{ + gStyle->SetOptStat(0); + + TFile* f_qq_ref = TFile::Open(qqZH_refinedWs); + TFile* f_gg_ref = TFile::Open(ggZH_refinedWs); + TFile* f_wz_ref = TFile::Open(WZ_refinedWs); + TFile* f_qq_norm = TFile::Open(qqZH_normalWs); + TFile* f_gg_norm = TFile::Open(ggZH_normalWs); + TFile* f_wz_norm = TFile::Open(WZ_normalWs); + + if (!f_qq_ref || !f_gg_ref || !f_qq_norm || !f_gg_norm || !f_wz_norm || !f_wz_ref) { + std::cerr << "Error opening one or more input files." << std::endl; + return; + } + + std::vector hnames = { + "h_E_lep1", + "h_E_lep2", + "h_E_lep3", + "h_pT_lep1", + "h_pT_lep2", + "h_pT_lep3", + "h_pZ_lep1", + "h_pZ_lep2", + "h_pZ_lep3", + "h_nJets", + "h_E_Jet1", + "h_E_Jet2", + "h_pT_Jet1", + "h_pT_Jet2", + "h_pZ_Jet1", + "h_pZ_Jet2", + "h_eTmiss", + "h_Hmass_T", + "h_Zmass", + "h_Whadmass", + "h_Wlepmass", + // "h_angle_WW", + "h_dPhi_WW", + // "h_angle_ZH", + "h_dPhi_ZH" + }; + + gSystem->mkdir("plots", kTRUE); + + for (const auto& hname : hnames) { + plotOne(f_qq_ref, f_gg_ref, f_qq_norm, f_gg_norm, f_wz_norm, f_wz_ref, hname); + } + + plotCutflowComparison(); +} \ No newline at end of file diff --git a/truth_plots_zh3l/plotting_ratio.c b/truth_plots_zh3l/plotting_ratio.c new file mode 100644 index 00000000..22a95844 --- /dev/null +++ b/truth_plots_zh3l/plotting_ratio.c @@ -0,0 +1,94 @@ +#include +#include +#include +#include +#include +#include +#include + +const char* f1_name = "./qqZH_gen_plots.root"; +const char* f2_name = "./ggZH_gen_plots.root"; +// const char* f1 = "/eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/ZH_Zto2L_Hto2WtoLNu2Q_results//reco_plots_refinedWHadReco.root"; +// const char* f2 = "/eos/user/d/dshekar/MCsamplesForBDTzh3l/Summer22EE_130x_nAODv12_Full2022v12/MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight/GluGluZH_Zto2L_Hto2WtoLNu2Q_results//reco_plots_refinedWHadReco.root"; +const char* histname = "h_dPhi_ZH"; + +gStyle->SetOptStat(0); + +TFile *f1 = TFile::Open(f1_name); +TFile *f2 = TFile::Open(f2_name); + +TH1D *h1 = (TH1D*)f1->Get(histname)->Clone("h1_clone"); +TH1D *h2 = (TH1D*)f2->Get(histname)->Clone("h2_clone"); + +h1->SetDirectory(nullptr); +h2->SetDirectory(nullptr); + +h1->SetStats(0); +h2->SetStats(0); + +h1->SetLineColor(kRed+1); +h1->SetLineWidth(2); +h2->SetLineColor(kBlue+1); +h2->SetLineWidth(2); + +h1->Scale(1.0 / h1->Integral()); +h2->Scale(1.0 / h2->Integral()); + +TH1D *hRatio = (TH1D*)h1->Clone("hRatio"); +hRatio->SetDirectory(nullptr); +hRatio->SetStats(0); +hRatio->Divide(h2); +hRatio->SetLineColor(kBlack); +hRatio->SetMarkerStyle(20); +hRatio->SetMarkerSize(0.8); + +TCanvas *c = new TCanvas("c", "overlay with ratio", 800, 800); + +TPad *pad1 = new TPad("pad1", "pad1", 0.0, 0.30, 1.0, 1.0); +pad1->SetBottomMargin(0.02); +pad1->SetLeftMargin(0.12); +pad1->SetRightMargin(0.04); +pad1->Draw(); +pad1->cd(); + +h1->SetMinimum(0.0); +h1->SetMaximum(0.3); +h1->Draw("hist"); +h2->Draw("hist same"); + +TLegend *leg = new TLegend(0.2, 0.5, 0.5, 0.7); +leg->SetBorderSize(0); +leg->SetFillStyle(0); +leg->AddEntry(h1, "qqZH", "l"); +leg->AddEntry(h2, "GluGluZH", "l"); +leg->Draw(); + +c->cd(); + +TPad *pad2 = new TPad("pad2", "pad2", 0.0, 0.05, 1.0, 0.30); +pad2->SetTopMargin(0.02); +pad2->SetBottomMargin(0.30); +pad2->SetLeftMargin(0.12); +pad2->SetRightMargin(0.04); +pad2->Draw(); +pad2->cd(); + +hRatio->GetXaxis()->SetTitle(h1->GetXaxis()->GetTitle()); +hRatio->GetYaxis()->SetTitle("Ratio"); +hRatio->GetYaxis()->SetNdivisions(505); +hRatio->GetYaxis()->SetTitleSize(0.12); +hRatio->GetYaxis()->SetTitleOffset(0.45); +hRatio->GetYaxis()->SetLabelSize(0.10); +hRatio->GetXaxis()->SetTitleSize(0.12); +hRatio->GetXaxis()->SetLabelSize(0.10); +hRatio->SetMinimum(0.0); +hRatio->SetMaximum(2.0); +hRatio->Draw("ep"); + +TLine *line = new TLine(hRatio->GetXaxis()->GetXmin(), 1.0, + hRatio->GetXaxis()->GetXmax(), 1.0); +line->SetLineStyle(2); +line->SetLineWidth(2); +line->Draw("same"); + +pad2->RedrawAxis(); diff --git a/truth_plots_zh3l/validation-genPlots.py b/truth_plots_zh3l/validation-genPlots.py new file mode 100644 index 00000000..7f598e1e --- /dev/null +++ b/truth_plots_zh3l/validation-genPlots.py @@ -0,0 +1,277 @@ +import ROOT +import math +import glob +import os +import argparse +from mkShapesRDF.lib.search_files import SearchFiles +searchFiles = SearchFiles() + +def invariant_mass(p4s): + total = ROOT.TLorentzVector() + for p4 in p4s: + total += p4 + return total.M() + +def angle_bw_vectors(v1, v2): + return v1.Angle(v2.Vect()) + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + return [(name, _files[:limitFiles])] + else: + return [(name, _files)] + +def makeMCDirectory(var=""): + _treeBaseDir = treeBaseDir + "" + if redirector != "": + _treeBaseDir = redirector + treeBaseDir + if var == "": + return "/".join([_treeBaseDir, mcProduction, mcSteps]) + else: + return "/".join([_treeBaseDir, mcProduction, mcSteps + "__" + var]) + +parser = argparse.ArgumentParser(description="Process NanoAOD ROOT files.") +parser.add_argument("--i", type=str, default='/eos/user/d/dshekar/MCsamplesForBDTzh3l', help="Path to the directory containing NanoAOD ROOT files.") +args = parser.parse_args() + +input_dir = args.i +print(f"Input directory set to: {input_dir}") +redirector = "" +mcProduction = 'Summer22EE_130x_nAODv12_Full2022v12' +mcSteps = 'MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight' +treeBaseDir = input_dir #f'/eos/user/d/dshekar/MCsamplesForBDTzh3l' +limitFiles = -1 + +FROM_HARD_PROCESS = 8 +IS_FIRST_COPY = 12 + +mcDirectory = makeMCDirectory() +samples = ["GluGluZH_Zto2L_Hto2WtoLNu2Q", "ZH_Zto2L_Hto2WtoLNu2Q"] + +for sample in samples: + save_dir = mcDirectory + "/" + str(sample)+'_results/' + if not os.path.isdir(save_dir): + os.makedirs(save_dir) + if not os.path.isdir(input_dir): + raise FileNotFoundError(f"The directory '{input_dir}' does not exist.") + + # Baseline kinematic histograms for leptons and jets + h_E_lep1 = ROOT.TH1F("h_E_lep1", "Gen Lep1 E;E [GeV];Events", 100, 0, 200) + h_E_lep2 = ROOT.TH1F("h_E_lep2", "Gen Lep2 E;E [GeV];Events", 100, 0, 200) + h_E_lep3 = ROOT.TH1F("h_E_lep3", "Gen Lep3 E;E [GeV];Events", 100, 0, 200) + h_pT_lep1 = ROOT.TH1F("h_pT_lep1", "Gen Lep1 pT;pT [GeV];Events", 100, 0, 200) + h_pT_lep2 = ROOT.TH1F("h_pT_lep2", "Gen Lep2 pT;pT [GeV];Events", 100, 0, 200) + h_pT_lep3 = ROOT.TH1F("h_pT_lep3", "Gen Lep3 pT;pT [GeV];Events", 100, 0, 200) + h_pz_lep1 = ROOT.TH1F("h_pZ_lep1", "Gen Lep1 pZ;pZ [GeV];Events", 100, 0, 200) + h_pz_lep2 = ROOT.TH1F("h_pZ_lep2", "Gen Lep2 pZ;pZ [GeV];Events", 100, 0, 200) + h_pz_lep3 = ROOT.TH1F("h_pZ_lep3", "Gen Lep3 pZ;pZ [GeV];Events", 100, 0, 200) + h_E_jet1 = ROOT.TH1F("h_E_Jet1", "Gen Jet1 E;E [GeV];Events", 100, 0, 200) + h_E_jet2 = ROOT.TH1F("h_E_Jet2", "Gen Jet2 E;E [GeV];Events", 100, 0, 200) + h_E_jet3 = ROOT.TH1F("h_E_Jet3", "Gen Jet3 E;E [GeV];Events", 100, 0, 200) + h_pT_jet1 = ROOT.TH1F("h_pT_Jet1", "Gen Jet1 pT;pT [GeV];Events", 100, 0, 200) + h_pT_jet2 = ROOT.TH1F("h_pT_Jet2", "Gen Jet2 pT;pT [GeV];Events", 100, 0, 200) + h_pT_jet3 = ROOT.TH1F("h_pT_Jet3", "Gen Jet3 pT;pT [GeV];Events", 100, 0, 200) + h_pz_jet1 = ROOT.TH1F("h_pZ_Jet1", "Gen Jet1 pZ;pZ [GeV];Events", 100, 0, 200) + h_pz_jet2 = ROOT.TH1F("h_pZ_Jet2", "Gen Jet2 pZ;pZ [GeV];Events", 100, 0, 200) + h_pz_jet3 = ROOT.TH1F("h_pZ_Jet3", "Gen Jet3 pZ;pZ [GeV];Events", 100, 0, 200) + h_eTmiss = ROOT.TH1F("h_eTmiss", "Gen MET;E_{T}^{miss} [GeV];Events", 100, 0, 200) + h_nLepWdecay = ROOT.TH1F("h_nLepWdecay", "nLep from Gen W decay;nLeptons;Events", 10, 0, 1) + # Histograms for W and Z mass, and angles between W-W and Z-H + h_Hmass = ROOT.TH1F("h_Hmass", "H mass;Mass [GeV];Events", 100, 0, 200) + h_zmass = ROOT.TH1F("h_zmass", "Gen Z mass;Mass [GeV];Events", 100, 0, 200) + h_angle_WW = ROOT.TH1F("h_angle_WW", "Angle between Ws;Angle [rad];Events", 64, 0, math.pi) + h_dPhi_WW = ROOT.TH1F("h_dPhi_WW", "Phi angle between Ws;Angle [rad];Events", 64, 0, math.pi) + h_angle_ZH = ROOT.TH1F("h_angle_ZH", "Angle between Z and H;Angle [rad];Events", 64, 0, math.pi) + h_dPhi_ZH = ROOT.TH1F("h_dPhi_ZH", "Phi angle between Z and H;Angle [rad];Events", 64, 0, math.pi) + + n_H = 0 + n_W = 0 + n_WfromH = 0 + n_W_leptonic = 0 + n_W_hadronic = 0 + n_Z = 0 + n_Z_leptonic = 0 + n_Z_hadronic = 0 + + root_files = nanoGetSampleFiles(mcDirectory, sample)[0][1] + print(f"Found ROOT files ({root_files}) in {input_dir} for sample {sample}.") + # root_files = glob.glob(os.path.join(input_dir, "*.root")) + for root_file_iter, root_file_name in enumerate(root_files): + if root_file_iter % 10 == 0: + print(f"Processing file {root_file_iter + 1}/{len(root_files)}: {root_file_name}") + file = ROOT.TFile.Open(root_file_name) + tree = file.Get("Events") + + for event in tree: + nGenPart = event.nGenPart + pdgId = list(event.GenPart_pdgId) + statusFlags = list(event.GenPart_statusFlags) + pt = list(event.GenPart_pt) + eta = list(event.GenPart_eta) + phi = list(event.GenPart_phi) + mass = list(event.GenPart_mass) + motherIdx = list(event.GenPart_genPartIdxMother) + + w_candidates = [] + z_candidates = [] + h_candidates = [] + all_particles = [] + + for i in range(nGenPart): + flag = statusFlags[i] + abs_pdg = abs(pdgId[i]) + p4 = ROOT.TLorentzVector() + p4.SetPtEtaPhiM(pt[i], eta[i], phi[i], mass[i]) + all_particles.append((i, pdgId[i], motherIdx[i], p4)) + + if not (flag & (1 << FROM_HARD_PROCESS)): + continue + if not (flag & (1 << IS_FIRST_COPY)): + continue + + if abs_pdg == 25: + h_candidates.append((i, p4)) + n_H += 1 + elif abs_pdg == 24: + w_candidates.append((i, p4, motherIdx[i], pdgId[i])) + n_W += 1 + elif abs_pdg == 23: + z_candidates.append((i, p4, motherIdx[i])) + n_Z += 1 + + # ----------- Higgs -> WW ----------- + for i, w1, m1, pdg1 in w_candidates: + for j, w2, m2, pdg2 in w_candidates: + if i >= j: continue + if m1 == m2 and m1 >= 0 and abs(pdgId[m1]) == 25: + # Fill mass and angle + h_Hmass.Fill(invariant_mass([w1, w2])) + n_WfromH += 1 + h_angle_WW.Fill(angle_bw_vectors(w1, w2)) + h_dPhi_WW.Fill(abs(w1.Phi() - w2.Phi())) + + # For each W, check decay mode + for w_idx, w_p4, w_mother, w_pdg in [(i, w1, m1, pdg1), (j, w2, m2, pdg2)]: + # Find daughters of this W + daughters = [p for p in all_particles if p[2] == w_idx] + # Leptonic: has e, mu, tau daughter + if any(abs(d[1]) in [11,13,15] for d in daughters): + h_nLepWdecay.Fill(len([d for d in daughters if abs(d[1]) in [11,13,15]])) + n_W_leptonic += 1 + h_pT_lep3.Fill(daughters[0][3].Pt()) # Assuming the first lepton + h_pz_lep3.Fill(daughters[0][3].Pz()) + h_E_lep3.Fill(daughters[0][3].E()) + # if len(daughters) > 1: + # print("WARNING: More than one daughter found in (H->) W decay.") + + # Hadronic: has quark daughter + elif any(abs(d[1]) in range(1,7) for d in daughters): + n_W_hadronic += 1 + h_pT_jet1.Fill(daughters[0][3].Pt()) # Assuming the first jet + h_pz_jet1.Fill(daughters[0][3].Pz()) + h_E_jet1.Fill(daughters[0][3].E()) + h_pT_jet2.Fill(daughters[1][3].Pt()) # Assuming the second jet + h_pz_jet2.Fill(daughters[1][3].Pz()) + h_E_jet2.Fill(daughters[1][3].E()) + if len(daughters) > 2: + print("WARNING: More than two daughters found in (H->) W decay.") + h_pT_jet3.Fill(daughters[2][3].Pt()) + h_pz_jet3.Fill(daughters[2][3].Pz()) + h_E_jet3.Fill(daughters[2][3].E()) + break # Only one unique pair per event + + # ----------- Z -> ll or hadrons ----------- + # print(z_candidates) + for i, z, m1 in z_candidates: + # Find all daughters of this Z + daughters = [p for p in all_particles if p[2] == i and abs(p[1]) != 23] + # all_particles.append((i, pdgId[i], motherIdx[i], p4)) + # print(daughters) + # Leptonic: at least one lepton daughter + if any(abs(d[1]) in [11,13,15] for d in daughters): + n_Z_leptonic += 1 + h_pT_lep1.Fill(daughters[0][3].Pt()) # Assuming the first lepton + h_pz_lep1.Fill(daughters[0][3].Pz()) + h_E_lep1.Fill(daughters[0][3].E()) + h_pT_lep2.Fill(daughters[1][3].Pt()) # Assuming the second lepton + h_pz_lep2.Fill(daughters[1][3].Pz()) + h_E_lep2.Fill(daughters[1][3].E()) + if len(daughters) > 2: + print("WARNING: More than two daughters found in Z decay.") + # Hadronic: at least one quark daughter + elif any(abs(d[1]) in range(1,7) for d in daughters): + n_Z_hadronic += 1 + + # Try to find the Higgs in the same event for Z-H angle + if h_candidates: + h_p4 = h_candidates[0][1] + h_angle_ZH.Fill(angle_bw_vectors(z, h_p4)) + h_dPhi_ZH.Fill(abs(z.Phi() - h_p4.Phi())) + + # For mass plot, look for lepton pairs from this Z + z_leptons = [d[3] for d in daughters if abs(d[1]) in [11,13,15]] + if len(z_leptons) == 2: + h_zmass.Fill(invariant_mass(z_leptons)) + file.Close() + # Create output file and histograms + out = ROOT.TFile(str(save_dir)+"/gen_plots.root", "RECREATE") + h_E_lep1.Write() + h_E_lep2.Write() + h_E_lep3.Write() + h_pT_lep1.Write() + h_pT_lep2.Write() + h_pT_lep3.Write() + h_pz_lep1.Write() + h_pz_lep2.Write() + h_pz_lep3.Write() + h_E_jet1.Write() + h_E_jet2.Write() + h_E_jet3.Write() + h_pT_jet1.Write() + h_pT_jet2.Write() + h_pT_jet3.Write() + h_pz_jet1.Write() + h_pz_jet2.Write() + h_pz_jet3.Write() + h_eTmiss.Write() + h_nLepWdecay.Write() + h_Hmass.Write() + h_zmass.Write() + h_angle_WW.Write() + h_dPhi_WW.Write() + h_angle_ZH.Write() + h_dPhi_ZH.Write() + out.Close() + h_E_lep1.Reset() + h_E_lep2.Reset() + h_E_lep3.Reset() + h_pT_lep1.Reset() + h_pT_lep2.Reset() + h_pT_lep3.Reset() + h_pz_lep1.Reset() + h_pz_lep2.Reset() + h_pz_lep3.Reset() + h_E_jet1.Reset() + h_E_jet2.Reset() + h_E_jet3.Reset() + h_pT_jet1.Reset() + h_pT_jet2.Reset() + h_pT_jet3.Reset() + h_pz_jet1.Reset() + h_pz_jet2.Reset() + h_pz_jet3.Reset() + h_eTmiss.Reset() + h_nLepWdecay.Reset() + h_Hmass.Reset() + h_zmass.Reset() + h_angle_WW.Reset() + h_dPhi_WW.Reset() + h_angle_ZH.Reset() + h_dPhi_ZH.Reset() + print(f"Finished processing sample {sample}.") + print(f'Total Higgs candidates: {n_H}, total W candidates: {n_W}') + print(f'Total W candidates from H: {n_WfromH}\n Num of leptonic W decays: {n_W_leptonic}\n Num of hadronic W decays: {n_W_hadronic}') + print(f'Total Z candidates: {n_Z}\n Num of leptonic Z decays: {n_Z_leptonic}\n Num of hadronic Z decays: {n_Z_hadronic}') + print("Validation plots saved to validation_plots.root") + print(f"Histograms saved to {str(save_dir)}/gen_plots.root") \ No newline at end of file diff --git a/truth_plots_zh3l/validation-recoPlots-cutFlow.py b/truth_plots_zh3l/validation-recoPlots-cutFlow.py new file mode 100644 index 00000000..77588bec --- /dev/null +++ b/truth_plots_zh3l/validation-recoPlots-cutFlow.py @@ -0,0 +1,436 @@ +import ROOT +import math +import glob +import os +import argparse +import numpy as np +from mkShapesRDF.lib.search_files import SearchFiles +import json + +searchFiles = SearchFiles() + +class NumpyEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, np.ndarray): + return obj.tolist() + if isinstance(obj, np.generic): + return obj.item() + return super(NumpyEncoder, self).default(obj) + +def invariant_mass(p4s): + total = ROOT.TLorentzVector() + for p4 in p4s: + total += p4 + return total.M() + +def get_lepton_p4(pt, eta, phi, mass): + p4 = ROOT.TLorentzVector() + p4.SetPtEtaPhiM(pt, eta, phi, mass) + return p4 + +def get_jet_p4(pt, eta, phi, mass): + p4 = ROOT.TLorentzVector() + p4.SetPtEtaPhiM(pt, eta, phi, mass) + return p4 + +def deltaPhi(phi1, phi2): + dphi = phi1 - phi2 + while dphi > math.pi: dphi -= 2*math.pi + while dphi < -math.pi: dphi += 2*math.pi + return abs(dphi) + +def w_transverse_mass(pt_lep, phi_lep, met, met_phi): + dphi = deltaPhi(phi_lep, met_phi) + return math.sqrt(2 * pt_lep * met * (1 - math.cos(dphi))) + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + return [(name, _files[:limitFiles])] + else: + return [(name, _files)] + +def makeMCDirectory(var=""): + _treeBaseDir = treeBaseDir + "" + if redirector != "": + _treeBaseDir = redirector + treeBaseDir + if var == "": + return "/".join([_treeBaseDir, mcProduction, mcSteps]) + else: + return "/".join([_treeBaseDir, mcProduction, mcSteps + "__" + var]) + +def getSampleBaseWeight(sample_files): + # baseW (stored in post-processed nanoAOD files) is a single value for an entire sample and not an event-by-event value + if isinstance(sample_files[0], list): + all_files = [f for sublist in sample_files for f in sublist] + else: + all_files = sample_files + # Sum genEventSumw across all files of a sample to get the global total of genWeights + global_df = ROOT.RDataFrame("Runs", all_files) + global_sumw = global_df.Sum("genEventSumw").GetValue() + if global_sumw == 0: + print("Warning: Global sumw is 0 for this sample!") + return 1.0 + + # Open just ONE file to reverse-calculate the cross section (xs) + first_file = all_files[0] + file_df = ROOT.RDataFrame("Runs", first_file) + file_sumw = file_df.Sum("genEventSumw").GetValue() + f = ROOT.TFile.Open(first_file) + t = f.Get("Events") + t.GetEntry(0) + basew_local = float(t.baseW) + f.Close() + # (baseW = xs * 1000 / file_sumw, so xs_factor = baseW * file_sumw). the 1000 comes to convert picobarn to femtobarn + xs_factor = basew_local * file_sumw + return xs_factor / global_sumw + +def addSampleWeight(samples, proc, sample, weight): + samples[proc]["weights"][sample] = weight + +def CombineBaseW(samples, proc, samplelist): + _filtFiles = list(filter(lambda k: k[0] in samplelist, samples[proc]["name"])) + _files = list(map(lambda k: k[1], _filtFiles)) + _l = list(map(lambda k: len(k), _files)) + leastFiles = _files[_l.index(min(_l))] + dfSmall = ROOT.RDataFrame("Runs", leastFiles) + s = dfSmall.Sum("genEventSumw").GetValue() + f = ROOT.TFile(leastFiles[0]) + t = f.Get("Events") + t.GetEntry(1) + xs = t.baseW * s + + __files = [] + for f_ in _files: + __files += f_ + df = ROOT.RDataFrame("Runs", __files) + s = df.Sum("genEventSumw").GetValue() + newbaseW = str(xs / s) + weight = newbaseW + "/baseW" + + for iSample in samplelist: + addSampleWeight(samples, proc, iSample, weight) + +parser = argparse.ArgumentParser(description="Process NanoAOD ROOT files.") +parser.add_argument("--i", type=str, default="/eos/user/d/dshekar/MCsamplesForBDTzh3l", + help="Path to the directory containing NanoAOD ROOT files.") +parser.add_argument("--lumi", type=float, default=26.7, + help="Integrated luminosity in inverse fb or pb^-1 depending on your xsec units.") +args = parser.parse_args() + +input_dir = args.i +lumi = args.lumi +print(f"Input directory set to: {input_dir}") +print(f"Luminosity set to: {lumi}") + +redirector = "" +mcProduction = 'Summer22EE_130x_nAODv12_Full2022v12' +mcSteps = 'MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight' +treeBaseDir = input_dir #f'/eos/user/d/dshekar/MCsamplesForBDTzh3l' +limitFiles = -1 +Whad_recoMethodology = 'refined' # 'normal' or 'refined' +FROM_HARD_PROCESS = 8 +IS_FIRST_COPY = 12 +w_mass = 80.4 # W boson mass in GeV +z_mass = 91.1876 # Z boson mass in GeV + + +mcDirectory = makeMCDirectory() +samples = ["GluGluZH_Zto2L_Hto2WtoLNu2Q", "ZH_Zto2L_Hto2WtoLNu2Q", "WZ"] # WZTo3LNu +cutflow_vs_sample = {} + +for sample in samples: + save_dir = mcDirectory + "/" + str(sample)+'_results/' + cutflow_full_sample = np.zeros(7) # Total number of events, after preselections, low-mass resonance, z mass selection, Zg veto, jet preselection, signal region. + if not os.path.isdir(save_dir): + os.makedirs(save_dir) + if not os.path.isdir(input_dir): + raise FileNotFoundError(f"The directory '{input_dir}' does not exist.") + + with ROOT.TFile(str(save_dir)+"/reco_plots_"+str(Whad_recoMethodology)+"WHadReco-cutFlow.root", "RECREATE") as out: + # Baseline kinematic histograms for leptons and jets + h_nLeps = ROOT.TH1F("h_nLeps", "Reco # leptons, pT> 10 (no other sel); n_{L}; Events", 20, 0, 20) + h_E_lep1 = ROOT.TH1F("h_E_lep1", "Reco Lep1 E;E [GeV];Events", 100, 0, 200) + h_E_lep2 = ROOT.TH1F("h_E_lep2", "Reco Lep2 E;E [GeV];Events", 100, 0, 200) + h_E_lep3 = ROOT.TH1F("h_E_lep3", "Reco Lep3 E;E [GeV];Events", 100, 0, 200) + h_pT_lep1 = ROOT.TH1F("h_pT_lep1", "Reco Lep1 pT;pT [GeV];Events", 100, 0, 200) + h_pT_lep2 = ROOT.TH1F("h_pT_lep2", "Reco Lep2 pT;pT [GeV];Events", 100, 0, 200) + h_pT_lep3 = ROOT.TH1F("h_pT_lep3", "Reco Lep3 pT;pT [GeV];Events", 100, 0, 200) + h_pz_lep1 = ROOT.TH1F("h_pZ_lep1", "Reco Lep1 pZ;pZ [GeV];Events", 100, 0, 200) + h_pz_lep2 = ROOT.TH1F("h_pZ_lep2", "Reco Lep2 pZ;pZ [GeV];Events", 100, 0, 200) + h_pz_lep3 = ROOT.TH1F("h_pZ_lep3", "Reco Lep3 pZ;pZ [GeV];Events", 100, 0, 200) + h_nJets = ROOT.TH1F("h_nJets", "Reco # jets, pT>30, eta<4.7 (no other sel); n_{j}; Events", 20, 0, 20) + h_E_jet1 = ROOT.TH1F("h_E_Jet1", "Reco Jet1 E;E [GeV];Events", 100, 0, 200) + h_E_jet2 = ROOT.TH1F("h_E_Jet2", "Reco Jet2 E;E [GeV];Events", 100, 0, 200) + h_pT_jet1 = ROOT.TH1F("h_pT_Jet1", "Reco Jet1 pT;pT [GeV];Events", 100, 0, 200) + h_pT_jet2 = ROOT.TH1F("h_pT_Jet2", "Reco Jet2 pT;pT [GeV];Events", 100, 0, 200) + h_pz_jet1 = ROOT.TH1F("h_pZ_Jet1", "Reco Jet1 pZ;pZ [GeV];Events", 100, 0, 200) + h_pz_jet2 = ROOT.TH1F("h_pZ_Jet2", "Reco Jet2 pZ;pZ [GeV];Events", 100, 0, 200) + h_eTmiss = ROOT.TH1F("h_eTmiss", "Reco MET;E_{T}^{miss} [GeV];Events", 100, 0, 200) + # h_nLepWdecay = ROOT.TH1F("h_nLepWdecay", "Reconstructed number of leptons from Reco W;nLeptons_{W};Events", 10, 0, 1) + + # Histograms for W and Z mass, and angles between W-W and Z-H + # h_Hmass = ROOT.TH1F("h_Hmass", "H mass;Mass [GeV];Events", 100, 0, 200) + h_Hmass_T = ROOT.TH1F("h_Hmass_T", "Reconstructed Higgs transverse mass; m^{H}_{T} [GeV]; Events", 60, 0, 600) + h_Zmass = ROOT.TH1F("h_Zmass", "Reconstructed Z mass; m_{LL} [GeV]; Events", 60, 60, 120) + h_Whadmass = ROOT.TH1F("h_Whadmass", "Reconstructed hadronic W mass; m_{jj} [GeV]; Events", 50, 0, 500) + h_Wlepmass_T = ROOT.TH1F("h_Wlepmass", "Reconstructed leptonic W transverse mass; m^{LNu}_{T} [GeV]; Events", 50, 0, 200) + h_angle_WW = ROOT.TH1F("h_angle_WW", "Angle between Ws;Angle [rad];Events", 64, 0, math.pi) + h_dPhi_WW = ROOT.TH1F("h_dPhi_WW", "Phi angle between Ws;Angle [rad];Events", 64, 0, math.pi) + h_angle_ZH = ROOT.TH1F("h_angle_ZH", "Angle between Z and H;Angle [rad];Events", 64, 0, math.pi) + h_dPhi_ZH = ROOT.TH1F("h_dPhi_ZH", "Phi angle between Z and H;Angle [rad];Events", 64, 0, math.pi) + + n_Z_candidates = 0 + n_W_lep_candidates = 0 + n_W_had_candidates = 0 + n_H_candidates = 0 + + root_files = nanoGetSampleFiles(mcDirectory, sample)[0][1] + print(f"Found ROOT files ({root_files}) in {input_dir} for sample {sample}.") + # root_files = glob.glob(os.path.join(input_dir, "*.root")) + + base_weight = 1.0 + if root_files: + try: + base_weight = getSampleBaseWeight(root_files) + except Exception: + print("Warning: Could not retrieve base weight from ROOT files. Using default base weight of 1.0.") + base_weight = 1.0 + + for root_file_iter, root_file_name in enumerate(root_files): + cutflow_full_file = np.zeros(7) + if root_file_iter % 10 == 0: + print(f"Processing file {root_file_iter + 1}/{len(root_files)}: {root_file_name}") + f = ROOT.TFile.Open(root_file_name) + tree = f.Get("Events") + for event in tree: + event_weight = 1.0 + if hasattr(event, "genWeight"): + event_weight *= event.genWeight + else: + print(f"Warning: Event does not have 'genWeight' attribute. Using default gen weight of 1.0.") + event_weight *= base_weight * lumi + cutflow_full_file[0] += event_weight # Total events + Z_candidate = None + phi_H_candidate = None + # --- Lepton selection (as in your table) --- + leptons = [] + for i in range(event.nElectron): + # if event.Electron_pt[i] > 25 and abs(event.Electron_eta[i]) < 2.5: + leptons.append({'pt': event.Electron_pt[i], 'eta': event.Electron_eta[i], 'phi': event.Electron_phi[i], 'mass': 0.000511, 'charge': event.Electron_charge[i], 'pdgId': 11}) + for i in range(event.nMuon): + # if event.Muon_pt[i] > 15 and abs(event.Muon_eta[i]) < 2.4: + leptons.append({'pt': event.Muon_pt[i], 'eta': event.Muon_eta[i], 'phi': event.Muon_phi[i], 'mass': 0.105, 'charge': event.Muon_charge[i], 'pdgId': 13}) + leptons = sorted(leptons, key=lambda x: -x['pt']) + n_leptons = sorted([lep for lep in leptons if lep['pt'] > 10], key=lambda x: -x['pt']) + if len(leptons) < 3: continue + if leptons[0]['pt'] < 25 or leptons[1]['pt'] < 20 or leptons[2]['pt'] < 15: continue + if len(leptons) > 3 and leptons[3]['pt'] > 10: continue + if abs(sum([lep['charge'] for lep in leptons[0:3]])) != 1: continue # If there's a 4th lepton with pT<10 then we need to check the charge sum of the leading 3 leptons + cutflow_full_file[1] += event_weight # After lepton pre-selections + # Min(mll) > 12 for all lepton pairs + pass_mll = True + for i in range(len(leptons)): + for j in range(i+1, len(leptons)): + l1 = get_lepton_p4(leptons[i]['pt'], leptons[i]['eta'], leptons[i]['phi'], leptons[i]['mass']) + l2 = get_lepton_p4(leptons[j]['pt'], leptons[j]['eta'], leptons[j]['phi'], leptons[j]['mass']) + if (l1 + l2).M() < 12: + pass_mll = False + if not pass_mll: continue + cutflow_full_file[2] += event_weight # After low-mass resonance veto + + # --- Z candidate: OSSF pair with |mll - mZ| < 25 --- + zcands = [] + for i in range(len(leptons)): + for j in range(i+1, len(leptons)): + if leptons[i]['pdgId'] != leptons[j]['pdgId']: continue + if leptons[i]['charge'] * leptons[j]['charge'] > 0: continue + l1 = get_lepton_p4(leptons[i]['pt'], leptons[i]['eta'], leptons[i]['phi'], leptons[i]['mass']) + l2 = get_lepton_p4(leptons[j]['pt'], leptons[j]['eta'], leptons[j]['phi'], leptons[j]['mass']) + mll = (l1 + l2).M() + if abs(mll - z_mass) < 25: + zcands.append((i, j, mll)) + if len(zcands) == 0: continue + cutflow_full_file[3] += event_weight # After Z mass window selection + zcand = min(zcands, key=lambda x: abs(x[2] - z_mass)) + z_leptons = [leptons[zcand[0]], leptons[zcand[1]]] + Z_candidate = get_lepton_p4(z_leptons[0]['pt'], z_leptons[0]['eta'], z_leptons[0]['phi'], z_leptons[0]['mass']) + get_lepton_p4(z_leptons[1]['pt'], z_leptons[1]['eta'], z_leptons[1]['phi'], z_leptons[1]['mass']) + n_Z_candidates += 1 + + # # --- b-jet veto --- + # has_bjet = False + # for i in range(event.nJet): + # if event.Jet_pt[i] > 20 and event.Jet_btagDeepB[i] > 0.4184: + # has_bjet = True + # if has_bjet: continue + + # --- Zγ veto: |m3l - mZ| > 20 GeV --- + third_lepton = [lep for k, lep in enumerate(leptons) if k not in [zcand[0], zcand[1]]][0] # Highest pT lepton that is not part of the Z candidate + + l3_p4 = get_lepton_p4(third_lepton['pt'], third_lepton['eta'], third_lepton['phi'], third_lepton['mass']) + z1_p4 = get_lepton_p4(z_leptons[0]['pt'], z_leptons[0]['eta'], z_leptons[0]['phi'], z_leptons[0]['mass']) + z2_p4 = get_lepton_p4(z_leptons[1]['pt'], z_leptons[1]['eta'], z_leptons[1]['phi'], z_leptons[1]['mass']) + m3l = (z1_p4 + z2_p4 + l3_p4).M() + if abs(m3l - z_mass) < 20: continue + cutflow_full_file[4] += event_weight # After Zγ veto + + # --- Jet selection --- + jets = [] + for i in range(event.nJet): + if event.Jet_pt[i] > 30 and abs(event.Jet_eta[i]) < 4.7: + jets.append({'pt': event.Jet_pt[i], 'eta': event.Jet_eta[i], 'phi': event.Jet_phi[i], 'mass': event.Jet_mass[i]}) + jets = sorted(jets, key=lambda x: -x['pt']) # Sort jets by descending pT + + # --- Signal region selection --- + signal_region = False + selected_w_pair = None + if len(jets) == 1: + cutflow_full_file[5] += event_weight # After jet preselection for 1-jet category + # Δφ(l + MET, j) < π/2 + l_met_px = third_lepton['pt']*math.cos(third_lepton['phi']) + event.MET_pt*math.cos(event.MET_phi) + l_met_py = third_lepton['pt']*math.sin(third_lepton['phi']) + event.MET_pt*math.sin(event.MET_phi) + l_met_phi = math.atan2(l_met_py, l_met_px) + dphi = deltaPhi(l_met_phi, jets[0]['phi']) + if dphi < math.pi/2: + signal_region = True + selected_w_pair = (0,-999) # Mark single jet as W candidate + elif len(jets) >= 2: + cutflow_full_file[5] += event_weight # After jet preselection for 2-jet category + if Whad_recoMethodology == 'refined': + # Generate all possible dijet pairs and compute |m_jj - 80.4| + dijet_pairs = [] + for i in range(len(jets)): + for j in range(i+1, len(jets)): + jet1_p4 = get_jet_p4(jets[i]['pt'], jets[i]['eta'], jets[i]['phi'], jets[i]['mass']) + jet2_p4 = get_jet_p4(jets[j]['pt'], jets[j]['eta'], jets[j]['phi'], jets[j]['mass']) + m_jj = (jet1_p4 + jet2_p4).M() + dijet_pairs.append({ + 'i': i, 'j': j, + 'dm': abs(m_jj - w_mass), + 'phi_jj': math.atan2( + jets[i]['pt']*math.sin(jets[i]['phi']) + jets[j]['pt']*math.sin(jets[j]['phi']), + jets[i]['pt']*math.cos(jets[i]['phi']) + jets[j]['pt']*math.cos(jets[j]['phi'])) + }) + + # Sort dijet pairs by proximity to W mass (smallest dm first) + dijet_pairs = sorted(dijet_pairs, key=lambda x: x['dm']) + + # Check pairs in order of ascending dm + for pair in dijet_pairs: + # Calculate Δφ(l+MET, dijet system) + l_met_px = third_lepton['pt']*math.cos(third_lepton['phi']) + event.MET_pt*math.cos(event.MET_phi) + l_met_py = third_lepton['pt']*math.sin(third_lepton['phi']) + event.MET_pt*math.sin(event.MET_phi) + l_met_phi = math.atan2(l_met_py, l_met_px) + dphi = deltaPhi(l_met_phi, pair['phi_jj']) + + if dphi < math.pi/2: + signal_region = True + selected_w_pair = (pair['i'], pair['j']) + break # Use first valid pair + + if not signal_region: + # hist_nWZcandidates.Fill(1) + continue # Skip event if no valid pairs + if Whad_recoMethodology == 'normal': + # Selecting 2 leading pT jets as the hadronic W decay products + # Δφ(l + MET, jj) < π/2 + px_jj = jets[0]['pt']*math.cos(jets[0]['phi']) + jets[1]['pt']*math.cos(jets[1]['phi']) + py_jj = jets[0]['pt']*math.sin(jets[0]['phi']) + jets[1]['pt']*math.sin(jets[1]['phi']) + phi_jj = math.atan2(py_jj, px_jj) + l_met_px = third_lepton['pt']*math.cos(third_lepton['phi']) + event.MET_pt*math.cos(event.MET_phi) + l_met_py = third_lepton['pt']*math.sin(third_lepton['phi']) + event.MET_pt*math.sin(event.MET_phi) + l_met_phi = math.atan2(l_met_py, l_met_px) + dphi = deltaPhi(l_met_phi, phi_jj) + if dphi < math.pi/2: + signal_region = True + selected_w_pair = (0, 1) # Mark leading two jets as W candidates + + + if not signal_region: + continue + cutflow_full_file[6] += event_weight # After signal region selection + # --- W (leptonic) reconstruction --- + lep_p4 = get_lepton_p4(third_lepton['pt'], third_lepton['eta'], third_lepton['phi'], third_lepton['mass']) + nu_p4 = ROOT.TLorentzVector() + nu_p4.SetPtEtaPhiM(event.MET_pt, 0, event.MET_phi, 0) + w_lep_mass = (lep_p4 + nu_p4).M() + w_lep_mt = w_transverse_mass(third_lepton['pt'], third_lepton['phi'], event.MET_pt, event.MET_phi) + n_W_lep_candidates += 1 + + # --- W (hadronic) reconstruction --- + had_w_p4 = None + w_i, w_j = selected_w_pair + + # 1-jet signal region: the jet is the hadronic W, if Δφ(l + MET, j) > π/2 then the prior IF statement would not have passed + had_w_p4 = get_jet_p4(jets[w_i]['pt'], jets[w_i]['eta'], jets[w_i]['phi'], jets[w_i]['mass']) + if w_j != -999: + # 2-jet signal region: + had_w_p4 = get_jet_p4(jets[w_i]['pt'], jets[w_i]['eta'], jets[w_i]['phi'], jets[w_i]['mass']) + get_jet_p4(jets[w_j]['pt'], jets[w_j]['eta'], jets[w_j]['phi'], jets[w_j]['mass']) + + n_W_had_candidates += 1 + + if had_w_p4 is not None: + phi_W_lep = (lep_p4 + nu_p4).Phi()#lep_p4.Phi() + phi_W_had = had_w_p4.Phi() + + # Visible system: lepton + hadronic W + vis_p4 = (lep_p4 + nu_p4) + had_w_p4 #lep_p4 + had_w_p4 + m_vis = vis_p4.M() + pt_vis = vis_p4.Pt() + px_vis = vis_p4.Px() + py_vis = vis_p4.Py() + px_miss = event.MET_pt * math.cos(event.MET_phi) + py_miss = event.MET_pt * math.sin(event.MET_phi) + et_vis = math.sqrt(m_vis**2 + pt_vis**2) + et_miss = event.MET_sumEt #event.MET_pt + mt2 = (et_vis + et_miss)**2 - ((px_vis + px_miss)**2 + (py_vis + py_miss)**2) + mt_higgs = math.sqrt(mt2) if mt2 > 0 else 0. + + n_H_candidates += 1 + px_higgs = px_vis + px_miss + py_higgs = py_vis + py_miss + phi_H_candidate = math.atan2(py_higgs, px_higgs) + + if Z_candidate is None or phi_H_candidate is None: + continue + phi_Z = Z_candidate.Phi() + + h_nLeps.Fill(len(n_leptons), event_weight) + h_pT_lep1.Fill(z_leptons[0]['pt'], event_weight) + h_pT_lep2.Fill(z_leptons[1]['pt'], event_weight) + h_pz_lep1.Fill(get_lepton_p4(z_leptons[0]['pt'], z_leptons[0]['eta'], z_leptons[0]['phi'], z_leptons[0]['mass']).Pz(), event_weight) + h_pz_lep2.Fill(get_lepton_p4(z_leptons[1]['pt'], z_leptons[1]['eta'], z_leptons[1]['phi'], z_leptons[1]['mass']).Pz(), event_weight) + h_E_lep1.Fill(get_lepton_p4(z_leptons[0]['pt'], z_leptons[0]['eta'], z_leptons[0]['phi'], z_leptons[0]['mass']).E(), event_weight) + h_E_lep2.Fill(get_lepton_p4(z_leptons[1]['pt'], z_leptons[1]['eta'], z_leptons[1]['phi'], z_leptons[1]['mass']).E(), event_weight) + h_Zmass.Fill(zcand[2], event_weight) + h_pT_lep3.Fill(third_lepton['pt'], event_weight) + h_pz_lep3.Fill(get_lepton_p4(third_lepton['pt'], third_lepton['eta'], third_lepton['phi'], third_lepton['mass']).Pz(), event_weight) + h_E_lep3.Fill(get_lepton_p4(third_lepton['pt'], third_lepton['eta'], third_lepton['phi'], third_lepton['mass']).E(), event_weight) + h_nJets.Fill(len(jets), event_weight) + h_Wlepmass_T.Fill(w_lep_mt, event_weight) + h_Whadmass.Fill(had_w_p4.M(), event_weight) + h_pT_jet1.Fill(jets[w_i]['pt'], event_weight) + h_pz_jet1.Fill(get_jet_p4(jets[w_i]['pt'], jets[w_i]['eta'], jets[w_i]['phi'], jets[w_i]['mass']).Pz(), event_weight) + h_E_jet1.Fill(get_jet_p4(jets[w_i]['pt'], jets[w_i]['eta'], jets[w_i]['phi'], jets[w_i]['mass']).E(), event_weight) + if w_j != -999: + h_pT_jet2.Fill(jets[w_j]['pt'], event_weight) + h_pz_jet2.Fill(get_jet_p4(jets[w_j]['pt'], jets[w_j]['eta'], jets[w_j]['phi'], jets[w_j]['mass']).Pz(), event_weight) + h_E_jet2.Fill(get_jet_p4(jets[w_j]['pt'], jets[w_j]['eta'], jets[w_j]['phi'], jets[w_j]['mass']).E(), event_weight) + h_dPhi_WW.Fill(deltaPhi(phi_W_lep, phi_W_had), event_weight) # NOTE: Check if these variables are out of scope + h_Hmass_T.Fill(mt_higgs, event_weight) # NOTE: Check if these variables are out of scope + + h_dPhi_ZH.Fill(deltaPhi(phi_Z, phi_H_candidate), event_weight) + f.Close() + cutflow_full_sample += cutflow_full_file + # # Create output file and histograms + # out = ROOT.TFile(str(save_dir)+"/reco_plots.root", "RECREATE") + # out.Close() + out.Write() + cutflow_vs_sample[sample] = cutflow_full_sample + print(f"Number of Z candidates: {n_Z_candidates}") + print(f"Number of leptonic W candidates: {n_W_lep_candidates}") + print(f"Number of hadronic W candidates: {n_W_had_candidates}") + print(f"Number of H candidates: {n_H_candidates}") + print("Histograms saved to reco_plots_"+str(Whad_recoMethodology)+"WHadReco--cutFlow.root") + with open(str(save_dir)+"/recoPlot_"+str(Whad_recoMethodology)+"_cutFlow.json", "w") as file: + json.dump(cutflow_vs_sample, file, indent=4, cls=NumpyEncoder) \ No newline at end of file diff --git a/truth_plots_zh3l/validation-recoPlots.py b/truth_plots_zh3l/validation-recoPlots.py new file mode 100644 index 00000000..62490e46 --- /dev/null +++ b/truth_plots_zh3l/validation-recoPlots.py @@ -0,0 +1,351 @@ +import ROOT +import math +import glob +import os +import argparse +import numpy as np +from mkShapesRDF.lib.search_files import SearchFiles +searchFiles = SearchFiles() + +def invariant_mass(p4s): + total = ROOT.TLorentzVector() + for p4 in p4s: + total += p4 + return total.M() + +def get_lepton_p4(pt, eta, phi, mass): + p4 = ROOT.TLorentzVector() + p4.SetPtEtaPhiM(pt, eta, phi, mass) + return p4 + +def get_jet_p4(pt, eta, phi, mass): + p4 = ROOT.TLorentzVector() + p4.SetPtEtaPhiM(pt, eta, phi, mass) + return p4 + +def deltaPhi(phi1, phi2): + dphi = phi1 - phi2 + while dphi > math.pi: dphi -= 2*math.pi + while dphi < -math.pi: dphi += 2*math.pi + return abs(dphi) + +def w_transverse_mass(pt_lep, phi_lep, met, met_phi): + dphi = deltaPhi(phi_lep, met_phi) + return math.sqrt(2 * pt_lep * met * (1 - math.cos(dphi))) + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + return [(name, _files[:limitFiles])] + else: + return [(name, _files)] + +def makeMCDirectory(var=""): + _treeBaseDir = treeBaseDir + "" + if redirector != "": + _treeBaseDir = redirector + treeBaseDir + if var == "": + return "/".join([_treeBaseDir, mcProduction, mcSteps]) + else: + return "/".join([_treeBaseDir, mcProduction, mcSteps + "__" + var]) + +parser = argparse.ArgumentParser(description="Process NanoAOD ROOT files.") +parser.add_argument("--i", type=str, default='/eos/user/d/dshekar/MCsamplesForBDTzh3l', help="Path to the directory containing NanoAOD ROOT files.") +args = parser.parse_args() + +input_dir = args.i +print(f"Input directory set to: {input_dir}") +redirector = "" +mcProduction = 'Summer22EE_130x_nAODv12_Full2022v12' +mcSteps = 'MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight' +treeBaseDir = input_dir #f'/eos/user/d/dshekar/MCsamplesForBDTzh3l' +limitFiles = -1 +Whad_recoMethodology = 'normal' # 'normal' or 'refined' +FROM_HARD_PROCESS = 8 +IS_FIRST_COPY = 12 +w_mass = 80.4 # W boson mass in GeV +z_mass = 91.1876 # Z boson mass in GeV + +mcDirectory = makeMCDirectory() +samples = ["GluGluZH_Zto2L_Hto2WtoLNu2Q", "ZH_Zto2L_Hto2WtoLNu2Q", "WZ"] +cutflow_vs_sample = {} + +for sample in samples: + save_dir = mcDirectory + "/" + str(sample)+'_results/' + cutflow_full_sample = np.zeros(7) # Total number of events, after preselections, low-mass resonance, z mass selection, Zg veto, jet preselection, signal region. + with ROOT.TFile(str(save_dir)+"/reco_plots_"+str(Whad_recoMethodology)+"WHadReco.root", "RECREATE") as out: + if not os.path.isdir(save_dir): + os.makedirs(save_dir) + if not os.path.isdir(input_dir): + raise FileNotFoundError(f"The directory '{input_dir}' does not exist.") + + # Baseline kinematic histograms for leptons and jets + h_nLeps = ROOT.TH1F("h_nLeps", "Reco # leptons, pT> 10 (no other sel); n_{L}; Events", 20, 0, 20) + h_E_lep1 = ROOT.TH1F("h_E_lep1", "Reco Lep1 E;E [GeV];Events", 100, 0, 200) + h_E_lep2 = ROOT.TH1F("h_E_lep2", "Reco Lep2 E;E [GeV];Events", 100, 0, 200) + h_E_lep3 = ROOT.TH1F("h_E_lep3", "Reco Lep3 E;E [GeV];Events", 100, 0, 200) + h_pT_lep1 = ROOT.TH1F("h_pT_lep1", "Reco Lep1 pT;pT [GeV];Events", 100, 0, 200) + h_pT_lep2 = ROOT.TH1F("h_pT_lep2", "Reco Lep2 pT;pT [GeV];Events", 100, 0, 200) + h_pT_lep3 = ROOT.TH1F("h_pT_lep3", "Reco Lep3 pT;pT [GeV];Events", 100, 0, 200) + h_pz_lep1 = ROOT.TH1F("h_pZ_lep1", "Reco Lep1 pZ;pZ [GeV];Events", 100, 0, 200) + h_pz_lep2 = ROOT.TH1F("h_pZ_lep2", "Reco Lep2 pZ;pZ [GeV];Events", 100, 0, 200) + h_pz_lep3 = ROOT.TH1F("h_pZ_lep3", "Reco Lep3 pZ;pZ [GeV];Events", 100, 0, 200) + h_nJets = ROOT.TH1F("h_nJets", "Reco # jets, pT>30, eta<4.7 (no other sel); n_{j}; Events", 20, 0, 20) + h_E_jet1 = ROOT.TH1F("h_E_Jet1", "Reco Jet1 E;E [GeV];Events", 100, 0, 200) + h_E_jet2 = ROOT.TH1F("h_E_Jet2", "Reco Jet2 E;E [GeV];Events", 100, 0, 200) + h_pT_jet1 = ROOT.TH1F("h_pT_Jet1", "Reco Jet1 pT;pT [GeV];Events", 100, 0, 200) + h_pT_jet2 = ROOT.TH1F("h_pT_Jet2", "Reco Jet2 pT;pT [GeV];Events", 100, 0, 200) + h_pz_jet1 = ROOT.TH1F("h_pZ_Jet1", "Reco Jet1 pZ;pZ [GeV];Events", 100, 0, 200) + h_pz_jet2 = ROOT.TH1F("h_pZ_Jet2", "Reco Jet2 pZ;pZ [GeV];Events", 100, 0, 200) + h_eTmiss = ROOT.TH1F("h_eTmiss", "Reco MET;E_{T}^{miss} [GeV];Events", 100, 0, 200) + # h_nLepWdecay = ROOT.TH1F("h_nLepWdecay", "Reconstructed number of leptons from Reco W;nLeptons_{W};Events", 10, 0, 1) + + # Histograms for W and Z mass, and angles between W-W and Z-H + # h_Hmass = ROOT.TH1F("h_Hmass", "H mass;Mass [GeV];Events", 100, 0, 200) + h_Hmass_T = ROOT.TH1F("h_Hmass_T", "Reconstructed Higgs transverse mass; m^{H}_{T} [GeV]; Events", 60, 0, 600) + h_Zmass = ROOT.TH1F("h_Zmass", "Reconstructed Z mass; m_{LL} [GeV]; Events", 60, 60, 120) + h_Whadmass = ROOT.TH1F("h_Whadmass", "Reconstructed hadronic W mass; m_{jj} [GeV]; Events", 50, 0, 500) + h_Wlepmass_T = ROOT.TH1F("h_Wlepmass", "Reconstructed leptonic W transverse mass; m^{LNu}_{T} [GeV]; Events", 50, 0, 200) + h_angle_WW = ROOT.TH1F("h_angle_WW", "Angle between Ws;Angle [rad];Events", 64, 0, math.pi) + h_dPhi_WW = ROOT.TH1F("h_dPhi_WW", "Phi angle between Ws;Angle [rad];Events", 64, 0, math.pi) + h_angle_ZH = ROOT.TH1F("h_angle_ZH", "Angle between Z and H;Angle [rad];Events", 64, 0, math.pi) + h_dPhi_ZH = ROOT.TH1F("h_dPhi_ZH", "Phi angle between Z and H;Angle [rad];Events", 64, 0, math.pi) + + n_Z_candidates = 0 + n_W_lep_candidates = 0 + n_W_had_candidates = 0 + n_H_candidates = 0 + + root_files = nanoGetSampleFiles(mcDirectory, sample)[0][1] + print(f"Found ROOT files ({root_files}) in {input_dir} for sample {sample}.") + # root_files = glob.glob(os.path.join(input_dir, "*.root")) + for root_file_iter, root_file_name in enumerate(root_files): + cutflow_full_file = np.zeros(7) + if root_file_iter % 10 == 0: + print(f"Processing file {root_file_iter + 1}/{len(root_files)}: {root_file_name}") + f = ROOT.TFile.Open(root_file_name) + tree = f.Get("Events") + for event in tree: + cutflow_full_file[0] += 1 # Total events + Z_candidate = None + phi_H_candidate = None + # --- Lepton selection (as in your table) --- + leptons = [] + for i in range(event.nElectron): + # if event.Electron_pt[i] > 25 and abs(event.Electron_eta[i]) < 2.5: + leptons.append({'pt': event.Electron_pt[i], 'eta': event.Electron_eta[i], 'phi': event.Electron_phi[i], 'mass': 0.000511, 'charge': event.Electron_charge[i], 'pdgId': 11}) + for i in range(event.nMuon): + # if event.Muon_pt[i] > 15 and abs(event.Muon_eta[i]) < 2.4: + leptons.append({'pt': event.Muon_pt[i], 'eta': event.Muon_eta[i], 'phi': event.Muon_phi[i], 'mass': 0.105, 'charge': event.Muon_charge[i], 'pdgId': 13}) + leptons = sorted(leptons, key=lambda x: -x['pt']) + n_leptons = sorted([lep for lep in leptons if lep['pt'] > 10], key=lambda x: -x['pt']) + h_nLeps.Fill(len(n_leptons)) + if len(leptons) < 3: continue + if leptons[0]['pt'] < 25 or leptons[1]['pt'] < 20 or leptons[2]['pt'] < 15: continue + if len(leptons) > 3 and leptons[3]['pt'] > 10: continue + if abs(sum([lep['charge'] for lep in leptons[0:3]])) != 1: continue # If there's a 4th lepton with pT<10 then we need to check the charge sum of the leading 3 leptons + cutflow_full_file[1] += 1 # After lepton pre-selections + # Min(mll) > 12 for all lepton pairs + pass_mll = True + for i in range(len(leptons)): + for j in range(i+1, len(leptons)): + l1 = get_lepton_p4(leptons[i]['pt'], leptons[i]['eta'], leptons[i]['phi'], leptons[i]['mass']) + l2 = get_lepton_p4(leptons[j]['pt'], leptons[j]['eta'], leptons[j]['phi'], leptons[j]['mass']) + if (l1 + l2).M() < 12: + pass_mll = False + if not pass_mll: continue + cutflow_full_file[2] += 1 # After low-mass resonance veto + + # --- Z candidate: OSSF pair with |mll - mZ| < 25 --- + zcands = [] + for i in range(len(leptons)): + for j in range(i+1, len(leptons)): + if leptons[i]['pdgId'] != leptons[j]['pdgId']: continue + if leptons[i]['charge'] * leptons[j]['charge'] > 0: continue + l1 = get_lepton_p4(leptons[i]['pt'], leptons[i]['eta'], leptons[i]['phi'], leptons[i]['mass']) + l2 = get_lepton_p4(leptons[j]['pt'], leptons[j]['eta'], leptons[j]['phi'], leptons[j]['mass']) + mll = (l1 + l2).M() + if abs(mll - z_mass) < 25: + zcands.append((i, j, mll)) + if len(zcands) == 0: continue + cutflow_full_file[3] += 1 # After Z mass window selection + zcand = min(zcands, key=lambda x: abs(x[2] - z_mass)) + z_leptons = [leptons[zcand[0]], leptons[zcand[1]]] + h_pT_lep1.Fill(z_leptons[0]['pt']) + h_pT_lep2.Fill(z_leptons[1]['pt']) + h_pz_lep1.Fill(get_lepton_p4(z_leptons[0]['pt'], z_leptons[0]['eta'], z_leptons[0]['phi'], z_leptons[0]['mass']).Pz()) + h_pz_lep2.Fill(get_lepton_p4(z_leptons[1]['pt'], z_leptons[1]['eta'], z_leptons[1]['phi'], z_leptons[1]['mass']).Pz()) + h_E_lep1.Fill(get_lepton_p4(z_leptons[0]['pt'], z_leptons[0]['eta'], z_leptons[0]['phi'], z_leptons[0]['mass']).E()) + h_E_lep2.Fill(get_lepton_p4(z_leptons[1]['pt'], z_leptons[1]['eta'], z_leptons[1]['phi'], z_leptons[1]['mass']).E()) + h_Zmass.Fill(zcand[2]) + Z_candidate = get_lepton_p4(z_leptons[0]['pt'], z_leptons[0]['eta'], z_leptons[0]['phi'], z_leptons[0]['mass']) + get_lepton_p4(z_leptons[1]['pt'], z_leptons[1]['eta'], z_leptons[1]['phi'], z_leptons[1]['mass']) + n_Z_candidates += 1 + + # # --- b-jet veto --- + # has_bjet = False + # for i in range(event.nJet): + # if event.Jet_pt[i] > 20 and event.Jet_btagDeepB[i] > 0.4184: + # has_bjet = True + # if has_bjet: continue + + # --- Zγ veto: |m3l - mZ| > 20 GeV --- + third_lepton = [lep for k, lep in enumerate(leptons) if k not in [zcand[0], zcand[1]]][0] # Highest pT lepton that is not part of the Z candidate + h_pT_lep3.Fill(third_lepton['pt']) + h_pz_lep3.Fill(get_lepton_p4(third_lepton['pt'], third_lepton['eta'], third_lepton['phi'], third_lepton['mass']).Pz()) + h_E_lep3.Fill(get_lepton_p4(third_lepton['pt'], third_lepton['eta'], third_lepton['phi'], third_lepton['mass']).E()) + + l3_p4 = get_lepton_p4(third_lepton['pt'], third_lepton['eta'], third_lepton['phi'], third_lepton['mass']) + z1_p4 = get_lepton_p4(z_leptons[0]['pt'], z_leptons[0]['eta'], z_leptons[0]['phi'], z_leptons[0]['mass']) + z2_p4 = get_lepton_p4(z_leptons[1]['pt'], z_leptons[1]['eta'], z_leptons[1]['phi'], z_leptons[1]['mass']) + m3l = (z1_p4 + z2_p4 + l3_p4).M() + if abs(m3l - z_mass) < 20: continue + cutflow_full_file[4] += 1 # After Zγ veto + + # --- Jet selection --- + jets = [] + for i in range(event.nJet): + if event.Jet_pt[i] > 30 and abs(event.Jet_eta[i]) < 4.7: + jets.append({'pt': event.Jet_pt[i], 'eta': event.Jet_eta[i], 'phi': event.Jet_phi[i], 'mass': event.Jet_mass[i]}) + h_nJets.Fill(len(jets)) + jets = sorted(jets, key=lambda x: -x['pt']) # Sort jets by descending pT + + # --- Signal region selection --- + signal_region = False + selected_w_pair = None + if len(jets) == 1: + cutflow_full_file[5] += 1 # After jet preselection for 1-jet category + # Δφ(l + MET, j) < π/2 + l_met_px = third_lepton['pt']*math.cos(third_lepton['phi']) + event.MET_pt*math.cos(event.MET_phi) + l_met_py = third_lepton['pt']*math.sin(third_lepton['phi']) + event.MET_pt*math.sin(event.MET_phi) + l_met_phi = math.atan2(l_met_py, l_met_px) + dphi = deltaPhi(l_met_phi, jets[0]['phi']) + if dphi < math.pi/2: + signal_region = True + selected_w_pair = (0,) # Mark single jet as W candidate + elif len(jets) >= 2: + cutflow_full_file[5] += 1 # After jet preselection for 2-jet category + if Whad_recoMethodology == 'refined': + # Generate all possible dijet pairs and compute |m_jj - 80.4| + dijet_pairs = [] + for i in range(len(jets)): + for j in range(i+1, len(jets)): + jet1_p4 = get_jet_p4(jets[i]['pt'], jets[i]['eta'], jets[i]['phi'], jets[i]['mass']) + jet2_p4 = get_jet_p4(jets[j]['pt'], jets[j]['eta'], jets[j]['phi'], jets[j]['mass']) + m_jj = (jet1_p4 + jet2_p4).M() + dijet_pairs.append({ + 'i': i, 'j': j, + 'dm': abs(m_jj - w_mass), + 'phi_jj': math.atan2( + jets[i]['pt']*math.sin(jets[i]['phi']) + jets[j]['pt']*math.sin(jets[j]['phi']), + jets[i]['pt']*math.cos(jets[i]['phi']) + jets[j]['pt']*math.cos(jets[j]['phi'])) + }) + + # Sort dijet pairs by proximity to W mass (smallest dm first) + dijet_pairs = sorted(dijet_pairs, key=lambda x: x['dm']) + + # Check pairs in order of ascending dm + for pair in dijet_pairs: + # Calculate Δφ(l+MET, dijet system) + l_met_px = third_lepton['pt']*math.cos(third_lepton['phi']) + event.MET_pt*math.cos(event.MET_phi) + l_met_py = third_lepton['pt']*math.sin(third_lepton['phi']) + event.MET_pt*math.sin(event.MET_phi) + l_met_phi = math.atan2(l_met_py, l_met_px) + dphi = deltaPhi(l_met_phi, pair['phi_jj']) + + if dphi < math.pi/2: + signal_region = True + selected_w_pair = (pair['i'], pair['j']) + break # Use first valid pair + + if not signal_region: + # hist_nWZcandidates.Fill(1) + continue # Skip event if no valid pairs + if Whad_recoMethodology == 'normal': + # Selecting 2 leading pT jets as the hadronic W decay products + # Δφ(l + MET, jj) < π/2 + px_jj = jets[0]['pt']*math.cos(jets[0]['phi']) + jets[1]['pt']*math.cos(jets[1]['phi']) + py_jj = jets[0]['pt']*math.sin(jets[0]['phi']) + jets[1]['pt']*math.sin(jets[1]['phi']) + phi_jj = math.atan2(py_jj, px_jj) + l_met_px = third_lepton['pt']*math.cos(third_lepton['phi']) + event.MET_pt*math.cos(event.MET_phi) + l_met_py = third_lepton['pt']*math.sin(third_lepton['phi']) + event.MET_pt*math.sin(event.MET_phi) + l_met_phi = math.atan2(l_met_py, l_met_px) + dphi = deltaPhi(l_met_phi, phi_jj) + if dphi < math.pi/2: + signal_region = True + selected_w_pair = (0, 1) # Mark leading two jets as W candidates + + + if not signal_region: + continue + cutflow_full_file[6] += 1 # After signal region selection + # --- W (leptonic) reconstruction --- + lep_p4 = get_lepton_p4(third_lepton['pt'], third_lepton['eta'], third_lepton['phi'], third_lepton['mass']) + nu_p4 = ROOT.TLorentzVector() + nu_p4.SetPtEtaPhiM(event.MET_pt, 0, event.MET_phi, 0) + w_lep_mass = (lep_p4 + nu_p4).M() + w_lep_mt = w_transverse_mass(third_lepton['pt'], third_lepton['phi'], event.MET_pt, event.MET_phi) + h_Wlepmass_T.Fill(w_lep_mt) + n_W_lep_candidates += 1 + + # --- W (hadronic) reconstruction --- + had_w_p4 = None + + if len(jets) == 1: + # 1-jet signal region: the jet is the hadronic W, if Δφ(l + MET, j) > π/2 then the prior IF statement would not have passed + had_w_p4 = get_jet_p4(jets[0]['pt'], jets[0]['eta'], jets[0]['phi'], jets[0]['mass']) + h_Whadmass.Fill(had_w_p4.M()) + h_pT_jet1.Fill(jets[0]['pt']) + h_pz_jet1.Fill(get_jet_p4(jets[0]['pt'], jets[0]['eta'], jets[0]['phi'], jets[0]['mass']).Pz()) + h_E_jet1.Fill(get_jet_p4(jets[0]['pt'], jets[0]['eta'], jets[0]['phi'], jets[0]['mass']).E()) + n_W_had_candidates += 1 + + elif len(jets) >= 2 and selected_w_pair is not None: + # 2-jet signal region: + i, j = selected_w_pair + had_w_p4 = get_jet_p4(jets[i]['pt'], jets[i]['eta'], jets[i]['phi'], jets[i]['mass']) + get_jet_p4(jets[j]['pt'], jets[j]['eta'], jets[j]['phi'], jets[j]['mass']) + h_Whadmass.Fill(had_w_p4.M()) + h_pT_jet1.Fill(jets[i]['pt']) + h_pT_jet2.Fill(jets[j]['pt']) + h_pz_jet1.Fill(get_jet_p4(jets[i]['pt'], jets[i]['eta'], jets[i]['phi'], jets[i]['mass']).Pz()) + h_pz_jet2.Fill(get_jet_p4(jets[j]['pt'], jets[j]['eta'], jets[j]['phi'], jets[j]['mass']).Pz()) + h_E_jet1.Fill(get_jet_p4(jets[i]['pt'], jets[i]['eta'], jets[i]['phi'], jets[i]['mass']).E()) + h_E_jet2.Fill(get_jet_p4(jets[j]['pt'], jets[j]['eta'], jets[j]['phi'], jets[j]['mass']).E()) + n_W_had_candidates += 1 + + if had_w_p4 is not None: + phi_W_lep = lep_p4.Phi() + phi_W_had = had_w_p4.Phi() + h_dPhi_WW.Fill(deltaPhi(phi_W_lep, phi_W_had)) + # Visible system: lepton + hadronic W + vis_p4 = lep_p4 + had_w_p4 + m_vis = vis_p4.M() + pt_vis = vis_p4.Pt() + px_vis = vis_p4.Px() + py_vis = vis_p4.Py() + px_miss = event.MET_pt * math.cos(event.MET_phi) + py_miss = event.MET_pt * math.sin(event.MET_phi) + et_vis = math.sqrt(m_vis**2 + pt_vis**2) + et_miss = event.MET_sumEt #event.MET_pt + mt2 = (et_vis + et_miss)**2 - ((px_vis + px_miss)**2 + (py_vis + py_miss)**2) + mt_higgs = math.sqrt(mt2) if mt2 > 0 else 0. + h_Hmass_T.Fill(mt_higgs) + n_H_candidates += 1 + px_higgs = px_vis + px_miss + py_higgs = py_vis + py_miss + phi_H_candidate = math.atan2(py_higgs, px_higgs) + + if Z_candidate is None or phi_H_candidate is None: + continue + phi_Z = Z_candidate.Phi() + h_dPhi_ZH.Fill(deltaPhi(phi_Z, phi_H_candidate)) + f.Close() + cutflow_full_sample += cutflow_full_file + # # Create output file and histograms + # out = ROOT.TFile(str(save_dir)+"/reco_plots.root", "RECREATE") + # out.Close() + out.Write() + cutflow_vs_sample[sample] = cutflow_full_sample + print(f"Number of Z candidates: {n_Z_candidates}") + print(f"Number of leptonic W candidates: {n_W_lep_candidates}") + print(f"Number of hadronic W candidates: {n_W_had_candidates}") + print(f"Number of H candidates: {n_H_candidates}") + print("Histograms saved to reco_plots.root") diff --git a/truth_plots_zh3l/validation-recoPlotsWhadDiJetSelection.py b/truth_plots_zh3l/validation-recoPlotsWhadDiJetSelection.py new file mode 100644 index 00000000..eead9067 --- /dev/null +++ b/truth_plots_zh3l/validation-recoPlotsWhadDiJetSelection.py @@ -0,0 +1,295 @@ +import ROOT +import math +import glob +import os +import argparse +from mkShapesRDF.lib.search_files import SearchFiles +searchFiles = SearchFiles() + + +def invariant_mass(p4s): + total = ROOT.TLorentzVector() + for p4 in p4s: + total += p4 + return total.M() + +def get_lepton_p4(pt, eta, phi, mass): + p4 = ROOT.TLorentzVector() + p4.SetPtEtaPhiM(pt, eta, phi, mass) + return p4 + +def get_jet_p4(pt, eta, phi, mass): + p4 = ROOT.TLorentzVector() + p4.SetPtEtaPhiM(pt, eta, phi, mass) + return p4 + +def deltaPhi(phi1, phi2): + dphi = phi1 - phi2 + while dphi > math.pi: dphi -= 2*math.pi + while dphi < -math.pi: dphi += 2*math.pi + return abs(dphi) + +def w_transverse_mass(pt_lep, phi_lep, met, met_phi): + dphi = deltaPhi(phi_lep, met_phi) + return math.sqrt(2 * pt_lep * met * (1 - math.cos(dphi))) + +def nanoGetSampleFiles(path, name): + _files = searchFiles.searchFiles(path, name, redirector=redirector) + if limitFiles != -1 and len(_files) > limitFiles: + return [(name, _files[:limitFiles])] + else: + return [(name, _files)] + +parser = argparse.ArgumentParser(description="Process NanoAOD ROOT files.") +parser.add_argument("--i", type=str, default='/eos/user/d/dshekar/MCsamplesForBDTzh3l', help="Path to the directory containing NanoAOD ROOT files.") +args = parser.parse_args() + +input_dir = args.i +print(f"Input directory set to: {input_dir}") +redirector = "" +mcProduction = 'Summer22EE_130x_nAODv12_Full2022v12' +mcSteps = 'MCl2loose2022EEv12__MCCorr2022EEv12JetScaling__l2tight' +treeBaseDir = input_dir #f'/eos/user/d/dshekar/MCsamplesForBDTzh3l' +limitFiles = 1 + + +def makeMCDirectory(var=""): + _treeBaseDir = treeBaseDir + "" + if redirector != "": + _treeBaseDir = redirector + treeBaseDir + if var == "": + return "/".join([_treeBaseDir, mcProduction, mcSteps]) + else: + return "/".join([_treeBaseDir, mcProduction, mcSteps + "__" + var]) + + +mcDirectory = makeMCDirectory() +samples = ["GluGluZH_Zto2L_Hto2WtoLNu2Q", "ZH_Zto2L_Hto2WtoLNu2Q"] + +for sample in samples: + save_dir = mcDirectory + "/" + str(sample)+'_results/' + if not os.path.isdir(save_dir): + os.makedirs(save_dir) + if not os.path.isdir(input_dir): + raise FileNotFoundError(f"The directory '{input_dir}' does not exist.") + + # Create output file and histograms + out = ROOT.TFile(str(save_dir)+"/reco_plots_WhadDiJetSelection.root", "RECREATE") + hist_nLeptons = ROOT.TH1F("hist_nLeptons", "Reconstructed number of leptons; n_{L}; Events", 20, 0, 20) + hist_nJets = ROOT.TH1F("hist_nJets", "Reconstructed number of jets; n_{j}; Events", 20, 0, 20) + hist_mZcandidate = ROOT.TH1F("hist_mZcandidate", "Reconstructed Z mass; m_{LL} [GeV]; Events", 60, 60, 120) + hist_mWhadronicCandidate = ROOT.TH1F("hist_mWhadronicCandidate", "Reconstructed hadronic W mass; m_{jj} [GeV]; Events", 50, 0, 500) + hist_mWleptonicCandidate = ROOT.TH1F("hist_mWleptonicCandidate", "Reconstructed leptonic W transverse mass; m^{LNu}_{T} [GeV]; Events", 50, 0, 200) + h_mH_T = ROOT.TH1F("h_mH_T", "Reconstructed Higgs transverse mass; m^{H}_{T} [GeV]; Events", 60, 0, 600) + hist_deltaPhi_ZH = ROOT.TH1F("hist_deltaPhi_ZH", "Delta Phi between Z and Higgs;delPhi [rad];Events", 50, -math.pi, math.pi) + hist_nZcandidates = ROOT.TH1F("hist_nZcandidates", "Number of Z-candidates per event; n_{L}; Events", 20, 0, 20) # Curious to know how many Z candidates we get per event on average with this selection (DS, 19May24) + hist_nWZcandidates = ROOT.TH1F("hist_nWZcandidates", "Number of WZ-candidates per event; n_{L}; Events", 20, 0, 20) # Curious to know how many WZ candidates we get per event on average with this selection (DS, 19May24) + + n_Z_candidates = 0 + n_W_lep_candidates = 0 + n_W_had_candidates = 0 + n_H_candidates = 0 + + w_mass = 80.4 # W boson mass in GeV + z_mass = 91.1876 + + + + root_files = nanoGetSampleFiles(mcDirectory, sample)[0][1] + print(f"Found ROOT files ({root_files}) in {input_dir} for sample {sample}.") + # root_files = glob.glob(os.path.join(input_dir, "*.root")) + for root_file_iter, root_file_name in enumerate(root_files): + if root_file_iter % 10 == 0: + print(f"Processing file {root_file_iter + 1}/{len(root_files)}: {root_file_name}") + f = ROOT.TFile.Open(root_file_name) + tree = f.Get("Events") + for event in tree: + Z_candidate = None + phi_H_candidate = None + # --- Lepton selection (as in your table) --- + leptons = [] + for i in range(event.nElectron): + # if event.Electron_pt[i] > 25 and abs(event.Electron_eta[i]) < 2.5: + leptons.append({'pt': event.Electron_pt[i], 'eta': event.Electron_eta[i], 'phi': event.Electron_phi[i], 'mass': 0.000511, 'charge': event.Electron_charge[i], 'pdgId': 11}) + for i in range(event.nMuon): + # if event.Muon_pt[i] > 15 and abs(event.Muon_eta[i]) < 2.4: + leptons.append({'pt': event.Muon_pt[i], 'eta': event.Muon_eta[i], 'phi': event.Muon_phi[i], 'mass': 0.105, 'charge': event.Muon_charge[i], 'pdgId': 13}) + leptons = sorted(leptons, key=lambda x: -x['pt']) + n_leptons = sorted([lep for lep in leptons if lep['pt'] > 10], key=lambda x: -x['pt']) + hist_nLeptons.Fill(len(n_leptons)) + if len(leptons) < 3: continue + if leptons[0]['pt'] < 25 or leptons[1]['pt'] < 20 or leptons[2]['pt'] < 15: continue + if len(leptons) > 3 and leptons[3]['pt'] > 10: continue + + # Min(mll) > 12 for all lepton pairs + pass_mll = True + for i in range(len(leptons)): + for j in range(i+1, len(leptons)): + l1 = get_lepton_p4(leptons[i]['pt'], leptons[i]['eta'], leptons[i]['phi'], leptons[i]['mass']) + l2 = get_lepton_p4(leptons[j]['pt'], leptons[j]['eta'], leptons[j]['phi'], leptons[j]['mass']) + if (l1 + l2).M() < 12: + pass_mll = False + if not pass_mll: continue + if abs(sum([lep['charge'] for lep in leptons])) != 1: continue + + # --- Z candidate: OSSF pair with |mll - mZ| < 25 --- + zcands = [] + for i in range(len(leptons)): + for j in range(i+1, len(leptons)): + if leptons[i]['pdgId'] != leptons[j]['pdgId']: continue + if leptons[i]['charge'] * leptons[j]['charge'] > 0: continue + l1 = get_lepton_p4(leptons[i]['pt'], leptons[i]['eta'], leptons[i]['phi'], leptons[i]['mass']) + l2 = get_lepton_p4(leptons[j]['pt'], leptons[j]['eta'], leptons[j]['phi'], leptons[j]['mass']) + mll = (l1 + l2).M() + if abs(mll - z_mass) < 25: + zcands.append((i, j, mll)) + if len(zcands) == 0: continue + + zcand = min(zcands, key=lambda x: abs(x[2] - z_mass)) + hist_nZcandidates.Fill(len(zcands)) + z_leptons = [leptons[zcand[0]], leptons[zcand[1]]] + hist_mZcandidate.Fill(zcand[2]) + Z_candidate = get_lepton_p4(z_leptons[0]['pt'], z_leptons[0]['eta'], z_leptons[0]['phi'], z_leptons[0]['mass']) + get_lepton_p4(z_leptons[1]['pt'], z_leptons[1]['eta'], z_leptons[1]['phi'], z_leptons[1]['mass']) + n_Z_candidates += 1 + + # # --- b-jet veto --- + # has_bjet = False + # for i in range(event.nJet): + # if event.Jet_pt[i] > 20 and event.Jet_btagDeepB[i] > 0.4184: + # has_bjet = True + # if has_bjet: continue + + # --- Zγ veto: |m3l - mZ| > 20 GeV --- + third_lepton = [lep for k, lep in enumerate(leptons) if k not in [zcand[0], zcand[1]]][0] + l3_p4 = get_lepton_p4(third_lepton['pt'], third_lepton['eta'], third_lepton['phi'], third_lepton['mass']) + z1_p4 = get_lepton_p4(z_leptons[0]['pt'], z_leptons[0]['eta'], z_leptons[0]['phi'], z_leptons[0]['mass']) + z2_p4 = get_lepton_p4(z_leptons[1]['pt'], z_leptons[1]['eta'], z_leptons[1]['phi'], z_leptons[1]['mass']) + m3l = (z1_p4 + z2_p4 + l3_p4).M() + if abs(m3l - z_mass) < 20: continue + + # --- Jet selection --- + jets = [] + for i in range(event.nJet): + if event.Jet_pt[i] > 30 and abs(event.Jet_eta[i]) < 4.7: + jets.append({'pt': event.Jet_pt[i], 'eta': event.Jet_eta[i], 'phi': event.Jet_phi[i], 'mass': event.Jet_mass[i]}) + hist_nJets.Fill(len(jets)) + jets = sorted(jets, key=lambda x: -x['pt']) # Sort jets by descending pT + + # --- Signal region selection and W reconstruction (revised) --- + # Initialize variables + signal_region = False + selected_w_pair = None + + if len(jets) >= 2: + # Generate all possible dijet pairs and compute |m_jj - 80.4| + dijet_pairs = [] + for i in range(len(jets)): + for j in range(i+1, len(jets)): + jet1_p4 = get_jet_p4(jets[i]['pt'], jets[i]['eta'], jets[i]['phi'], jets[i]['mass']) + jet2_p4 = get_jet_p4(jets[j]['pt'], jets[j]['eta'], jets[j]['phi'], jets[j]['mass']) + m_jj = (jet1_p4 + jet2_p4).M() + dijet_pairs.append({ + 'i': i, 'j': j, + 'dm': abs(m_jj - 80.4), + 'phi_jj': math.atan2( + jets[i]['pt']*math.sin(jets[i]['phi']) + jets[j]['pt']*math.sin(jets[j]['phi']), + jets[i]['pt']*math.cos(jets[i]['phi']) + jets[j]['pt']*math.cos(jets[j]['phi'])) + }) + + # Sort dijet pairs by proximity to W mass (smallest dm first) + dijet_pairs = sorted(dijet_pairs, key=lambda x: x['dm']) + + # Check pairs in order of ascending dm + for pair in dijet_pairs: + # Calculate Δφ(l+MET, dijet system) + l_met_px = third_lepton['pt']*math.cos(third_lepton['phi']) + event.MET_pt*math.cos(event.MET_phi) + l_met_py = third_lepton['pt']*math.sin(third_lepton['phi']) + event.MET_pt*math.sin(event.MET_phi) + l_met_phi = math.atan2(l_met_py, l_met_px) + dphi = deltaPhi(l_met_phi, pair['phi_jj']) + + if dphi < math.pi/2: + signal_region = True + selected_w_pair = (pair['i'], pair['j']) + break # Use first valid pair + + if not signal_region: + hist_nWZcandidates.Fill(1) + continue # Skip event if no valid pairs + + elif len(jets) == 1: + # Original 1-jet logic (unchanged) + l_met_px = third_lepton['pt']*math.cos(third_lepton['phi']) + event.MET_pt*math.cos(event.MET_phi) + l_met_py = third_lepton['pt']*math.sin(third_lepton['phi']) + event.MET_pt*math.sin(event.MET_phi) + l_met_phi = math.atan2(l_met_py, l_met_px) + dphi = deltaPhi(l_met_phi, jets[0]['phi']) + + if dphi < math.pi/2: + signal_region = True + selected_w_pair = (0,) # Mark single jet as W candidate + + if not signal_region: + hist_nWZcandidates.Fill(1) + continue + + # --- W (leptonic) reconstruction --- + lep_p4 = get_lepton_p4(third_lepton['pt'], third_lepton['eta'], third_lepton['phi'], third_lepton['mass']) + nu_p4 = ROOT.TLorentzVector() + nu_p4.SetPtEtaPhiM(event.MET_pt, 0, event.MET_phi, 0) + w_lep_mass = (lep_p4 + nu_p4).M() + w_lep_mt = w_transverse_mass(third_lepton['pt'], third_lepton['phi'], event.MET_pt, event.MET_phi) + hist_mWleptonicCandidate.Fill(w_lep_mt) + n_W_lep_candidates += 1 + + + # --- Reconstruct W and Higgs using selected jets --- + had_w_p4 = None + + if len(jets) == 1: + # 1-jet case + had_w_p4 = get_jet_p4(jets[0]['pt'], jets[0]['eta'], jets[0]['phi'], jets[0]['mass']) + hist_mWhadronicCandidate.Fill(had_w_p4.M()) + n_W_had_candidates += 1 + + elif len(jets) >= 2 and selected_w_pair is not None: + # Reconstruct from selected dijet pair + i, j = selected_w_pair + jet1_p4 = get_jet_p4(jets[i]['pt'], jets[i]['eta'], jets[i]['phi'], jets[i]['mass']) + jet2_p4 = get_jet_p4(jets[j]['pt'], jets[j]['eta'], jets[j]['phi'], jets[j]['mass']) + had_w_p4 = jet1_p4 + jet2_p4 + hist_mWhadronicCandidate.Fill(had_w_p4.M()) + n_W_had_candidates += 1 + + # --- Higgs reconstruction --- + if had_w_p4 is not None: + # Visible system: lepton + hadronic W + vis_p4 = lep_p4 + had_w_p4 + m_vis = vis_p4.M() + pt_vis = vis_p4.Pt() + px_vis = vis_p4.Px() + py_vis = vis_p4.Py() + px_miss = event.MET_pt * math.cos(event.MET_phi) + py_miss = event.MET_pt * math.sin(event.MET_phi) + et_vis = math.sqrt(m_vis**2 + pt_vis**2) + et_miss = event.MET_pt + mt2 = (et_vis + et_miss)**2 - ((px_vis + px_miss)**2 + (py_vis + py_miss)**2) + mt_higgs = math.sqrt(mt2) if mt2 > 0 else 0. + h_mH_T.Fill(mt_higgs) + n_H_candidates += 1 + px_higgs = px_vis + px_miss + py_higgs = py_vis + py_miss + phi_H_candidate = math.atan2(py_higgs, px_higgs) + + if Z_candidate is None or phi_H_candidate is None: + continue + phi_Z = Z_candidate.Phi() + hist_deltaPhi_ZH.Fill(deltaPhi(phi_Z, phi_H_candidate)) + f.Close() + + out.Write() + out.Close() + + print(f"Number of Z candidates: {n_Z_candidates}") + print(f"Number of leptonic W candidates: {n_W_lep_candidates}") + print(f"Number of hadronic W candidates: {n_W_had_candidates}") + print(f"Number of H candidates: {n_H_candidates}") + print(f"Histograms saved to {str(save_dir)}/reco_plots_WhadDiJetSelection.root")